Elsevier

Energy

Volume 141, 15 December 2017, Pages 1795-1809
Energy

Performance analysis of optimal designed hybrid energy systems for grid-connected nearly/net zero energy buildings

https://doi.org/10.1016/j.energy.2017.11.093Get rights and content

Highlights

  • Four optimal hybrid energy systems are compared for designing nZEBs.

  • Monte Carlo simulation is applied for uncertainty analysis.

  • Impact of weighting factor combinations on nZEBs performance is investigated.

  • Correlative dependency between design mismatch ratio and probability is identified.

  • PV& BDG system has a robust performance among the four HESs.

Abstract

Hybrid energy systems have provided a promising way to realize nearly/net zero energy buildings (nZEB) including isolated buildings in remote areas and grid-connected buildings. This study aims to investigate and compare several typical hybrid energy systems (HESs) for designing grid-connected nearly/net zero energy buildings (nZEB). Specially, an exhaustive searching method and Monte Carlo simulation are utilized to optimize hybrid energy systems for Hong Kong Zero Carbon Building considering uncertainty impacts. The performance of nearly/net zero energy buildings is evaluated in terms of a combined performance comprised of the cost, CO2 emissions and grid interaction index. Subsequently, the effects of design mismatch ratio, weighting factor combination and the probability to be nZEB on the performance are analyzed and compared under the four hybrid energy systems. The study results show that the probability for a building to achieve annual energy balance is highly depending on the design mismatch ratio, and the correlative dependencies between the two parameters are fitted in formulas for the studied hybrid energy systems. In addition, an nZEB designed with PV& BDG system is found to have a robust performance compared with that designed with other three hybrid energy systems under the same design condition.

Introduction

The world energy requirements and CO2 emissions would increase by 65% and 70% respectively between 1995 and 2020, as reported by International Energy Agency (IEA) [1]. Buildings consume about 40% of the total world energy production, which is strongly depending on the low cost fossil fuels resources. Renewable energy resources, as environmental friendly and alternative energy clean sources, are expected to contribute the world energy requirements from 14% at present up to 80% in 2100 [2]. Due to the intermittent nature of the renewable energy sources, hybrid energy systems (HESs) usually consist of two or more renewable energy sources together to provide a reliable power to the load. And the hybrid energy system is widely accepted as a promising approach for future buildings to achieve primary energy savings, pollution emissions reductions and sustainable low-carbon society [3], [4].

Recently, hybrid energy systems have been widespread utilized to provide power for rural and remote areas as well as micro-grid system applications [5], [6]. Different types of HESs, e.g. photovoltaic & wind turbine (PV&WT) [7], [8], [9], photovoltaic & wind turbine & diesel generator (PV&WT&BDG) [10], [11], [12], photovoltaic & wind turbine & battery or fuel cell (PV&WT&BAT or PV&WT&FC) [13], [14], [15], [16], [17], [18] etc, have been investigated in previous studies. In Ref. [7], a 30 kW PV&WT hybrid energy system dynamic model was presented to investigate the control strategy for a sustainable micro-grid application and it was finally demonstrated to be a feasible option for grid application. As indicated by Gomes et al. [8], the variability in non-dispatchable PV&WT power generation poses great challenges to the integration of renewable energy sources into the electricity power grid. Therefore, they formulated the PV&WT coordinated trading as a stochastic linear programming problem, and then obtained the optimal bidding strategy that maximizes the total profit. In order to achieve a fast and stable respond for the power grid control, a diesel engine and an intelligent controller were proposed for the PV &WT by Hong et al. [10], and it is demonstrated to be more efficiency and better transient as well as more stability even under different load conditions and disturbance. In another study, Dufo-López et al. [11] developed a new stochastic-heuristic methodology for optimal the electrical supply of off-grid photovoltaic & wind & diesel with battery storage, which takes into account uncertain parameters impact. Energy storage systems (e.g. battery or fuel cell) are usually employed to complement the renewable energy systems in stand-alone applications and shift the peak load to relieve the power imbalance in grid-connected buildings. Ma et al. [13] employed the HOMER software to conduct a feasibility study and techno-economic evaluation on a hybrid solar-wind system with battery energy storage for a standalone island, the optimal autonomous system configuration is finally obtained in terms of system net present cost and cost of energy. Moghaddam et al. [14] presented an expert multi-objective Adaptive Modified Particle Swarm Optimization algorithm (AMPSO) for optimal operation of a back-up Micro-Turbine/Fuel Cell/Battery hybrid power to minimize the total operating cost and the net emission simultaneously. By considering the daily energy consumption variations for winter and summer weekdays and weekends, Tazvinga et al. [15] conducted a study on the photovoltaic & diesel & battery model for remote consumers and compared it with the case where the diesel generator satisfies the load on its own. Yang et al. [16] presented a power management system of a household photovoltaic & battery hybrid power system within demand side management under time of use electricity tariff, and the proposed strategies can largely reduce energy cost and energy consumption from the grid. With the fast development of smart grids and “nearly/net zero energy buildings (nZEBs)” for future buildings, four typical HESs, i.e. PV&WT, PV&BDG, WT&BDG and PV&WT&BDG, are desirable design options for designing grid-connected nZEBs. Therefore, it is necessary and meaningful to evaluate and compare the performance of the four types of HESs, which can assist designers with system selection and design optimization for grid-connected nZEBs.

In the study of designing hybrid energy systems for buildings, control strategies applied for energy systems must be considered simultaneously. Two basic operation strategies are commonly applied for combined cooling, heating and power system (CCHP/CHP/CCP): following the electric load (FEL) and following the thermal load (FTL) [19], [20]. Other strategies for operating CCHP system such as operational strategy based on the ratio of the cooling generated to actual building cooling load [21] and a novel operation strategy aiming at minimizing an integrated index [22]. There are also considerable studies conducted on model predictive control (MPC)-based optimal scheduling of energy storage systems [23], [24] and distribute energy generation systems [3], [15], [25]. Zhao et al. [3] adopted the MPC method based on nonlinear programming algorithm programming to optimize the operation of integrated energy systems in low energy buildings under day-ahead electricity price. The proposed optimal scheduling strategy can help the building to achieve significant reductions in operation cost, primary energy consumption and CO2 emissions. In order to compare the corresponding fuel costs and evaluate the operational efficiency of the hybrid system for a 24-h period, Tazvinga et al. [15] investigated the photovoltaic & diesel & battery model for remote consumers by considering the daily energy consumption variations for winter and summer weekdays and weekends. The results show that it can achieve 73% and 77% fuel savings in winter and 80.5% and 82% fuel savings in summer for days considered when compared to the case where the diesel generator satisfies the load on its own.

Selection of evaluation criteria is also an important work necessary for evaluating the designed HES for an nZEB. The evaluation criteria considered in previous studies are classified into four aspects: technological factors (e.g. Feasibility, risk and reliability), economic factors (e.g. pollutant emission, land requirements), socio-political factors (e.g. political acceptance, social acceptance) and environmental factors (e.g. implementation cost, economic value) [26], [27]. Balaras et al. [28] studied 193 European apartment buildings on the environmental impact of energy consumption due to the building heating. They demonstrated that about 30% of the buildings had higher airborne emissions. Li et al. [29] conducted a techno-economic feasibility study on an autonomous hybrid PV&WT&battery power system for a household in Urumqi of China. The Hybrid Optimization Model for Electric Renewables (HOMER) simulation software was employed in this study to estimate the total net present cost (NPC) and the levelized cost of energy (COE) of the system. Dufo-López et al. [11] proposed a new stochastic-heuristic methodology for the optimization of stand-alone (off-grid) hybrid photovoltaic & wind & diesel with battery storage systems, and the aim is to minimize the net present cost of the system. Considering the random and intermittent nature of solar and wind source, the reliability of energy systems becomes an important issue. Loss of power supply probability (LPSP) is a widely used indicator to assess the system reliability [30], [31]. In the grid-connected nZEB, two-way information flow between the buildings and smart grid brings some new challenges for grid system and therefore the interaction should be considered and evaluated [32], [33], [34], [35]. Cao et al. [32] defined six matching indices based on the extension of two commonly used basic indices (i.e. on-site energy fraction and on-site energy matching), and these extended indices are demonstrated to be powerful tools for assessing the matching situation of complicated building energy systems. Deng et al. [33] conducted a review on the evaluation method for net zero energy buildings, the load matching and grid interaction are recommended to evaluate the NZEB performance on different time-scales. However, the features of different types of HESs are still lacking investigation in term of the interaction between the buildings and smart grid.

Previous studies on the size optimization of hybrid energy system are usually carried out using a deterministic approach and some take into account uncertainties in renewable sources. However, selection of HES for nZEB is an uphill task as it is not only dependent of the load requirements but also greatly affected by the randomly varied input parameters of the sources. Kamjoo et al. [36] optimized a PV & wind & battery system using Genetic Algorithm (GA) considering uncertainties by the method of chance-constrained programming (CCP) and comparing the results with Monte Carlo Simulation (MCS). In Ref. [37], Maheri investigated the reliability of different PV & wind & diesel & battery systems under the deterministic design method. In his later Ref. [38], two algorithms (with MCS) were employed to the optimization based on the margin of safety. Other studies on uncertainty analysis of renewable energy system design can be found in Refs. [39], [40], [41], [42].

Our previous study [43] has developed a robust optimal design method for sizing renewable energy systems in nZEB. Based on the developed robust optimal design method, this study aims to investigate and compare the robust performance of a nearly/net zero energy building designed with four typical HESs. This will help the system designer to select suitable design options while implementing hybrid energy systems for grid-connected buildings. The remainder of this paper is organized by five subsections as follows. Section 2 presents an outline of performance analysis of nZEB designed with different HESs when uncertainty is concerned. Section 3 describes the information of the studied building and energy systems. Section 4 discusses the obtained results in terms of four aspects. Finally, the conclusion of this study is given in Section 5.

Section snippets

Problem description

It is acknowledged that the hybrid energy system (HES) is one of the most important elements for buildings to be nZEB, and different types of HESs may be preferred for different conditions. As the target of annual energy balance for nZEB is highly depending on the selection of HES, it is meaningful to investigate how the design mismatch ratio affects the probability of achieving nZEB. In addition, uncertain parameters (e.g. solar radiation, wind velocity, ambient temperature etc.) may have

Building description

The Construction Industry Council (CIC) Zero-Carbon Building (ZCB) in Hong Kong is a net zero-carbon building designed in hot and humid climate zone, as shown in Fig. 3. The three-storey building includes a basement and it covers a total site area of 14,700 m2. In this building, 20% of energy saving is obtained through passive design technology (i.e. wind catcher, high performance glazing and ultra-low thermal transfer etc) and 25% of energy saving is obtained through green active energy

Results and discussions

In this study, Hong Kong Zero Carbon Building (ZCB) model is developed in TRNSYS and energy system models are developed based MATLAB. It aims to investigate the following four aspects: (1) Influence of the type of nZEB on optimal design mismatch ratio; (2) Influence of weighting factors on optimal design mismatch ratio; (3) The relationship between design mismatch ratio and the probability of achieving nZEB; (4) Evaluation of performance stability. The hourly renewable energy resources and

Conclusion

This study aims to investigate and compare the performance of a nearly/net zero energy building designed with four typical HESs which are optimal sized considering uncertainty. The uncertain parameters considered are solar radiation, wind velocity, building cooling load and other load. Monte Carlo simulation and exhaustive searching method are employed to find the optimal HES size concerning the uncertainty impacts. Influence of the selected weighting factors, design mismatch ratio, the

Acknowledgement

The authors acknowledge Hong Kong Zero Carbon Building for providing the building information and on-site monitored data. And also the support by Natural Science Foundation of China (Project No. 51608001 and Project No. 51478001) for the financial support to carry out the research work reported in this paper.

References (44)

  • T. Ma et al.

    A feasibility study of a stand-alone hybrid solar–wind–battery system for a remote island

    Appl Energy

    (2014)
  • N. Agarwal et al.

    Optimization of grid independent hybrid PV–diesel–battery system for power generation in remote villages of Uttar Pradesh, India

    Energy Sustain Dev

    (2013)
  • H. Tazvinga et al.

    Minimum cost solution of photovoltaic–diesel–battery hybrid power systems for remote consumers

    Sol Energy

    (2013)
  • F. Yang et al.

    Techno-economic and environmental optimization of a household photovoltaic-battery hybrid power system within demand side management

    Renew Energy

    (2017)
  • T.C. Ou et al.

    Dynamic operation and control of microgrid hybrid power systems

    Energy

    (2014)
  • M.R. Basir Khan et al.

    Optimal combination of solar, wind, micro-hydro and diesel systems based on actual seasonal load profiles for a resort island in the South China Sea

    Energy

    (2015)
  • J.J. Wang et al.

    Performance comparison of combined cooling heating and power system in different operation modes

    Appl Energy

    (2011)
  • L. Guo et al.

    A two-stage optimal planning and design method for combined cooling, heat and power microgrid system

    Energy Convers Manag

    (2013)
  • M.X. Liu et al.

    A new operation strategy for CCHP systems with hybrid chillers

    Appl Energy

    (2012)
  • C.Y. Zheng et al.

    A novel operation strategy for CCHP systems based on minimum distance

    Appl Energy

    (2014)
  • F. Berkenkamp et al.

    Hybrid model predictive control of stratified thermal storages in buildings

    Energy Build

    (2014)
  • S. Mitra et al.

    Optimal scheduling of industrial combined heat and power plants under time-sensitive electricity prices

    Energy

    (2013)
  • Cited by (43)

    • A critical review of the performance evaluation and optimization of grid interactions between zero-energy buildings and power grids

      2022, Sustainable Cities and Society
      Citation Excerpt :

      The following are some common stability indicators. The GI indicator represents the ratio of the real-time net electricity interaction value between buildings and the grid in a given period (e.g., monthly) to the annual maximum interaction value or to the average interaction value in a certain period (Huang et al., 2017; Voss et al., 2010). The standard deviation of the GI indicator can reflect the fluctuation of interactive electricity, and when the standard deviation is lower, the interaction is more stable.

    View all citing articles on Scopus
    View full text