Capacity design of a distributed energy system based on integrated optimization and operation strategy of exergy loss reduction

https://doi.org/10.1016/j.enconman.2020.113648Get rights and content

Highlights

  • Exergy loss rate is reduced in the off-design operation of components.

  • Hourly energy generation, storage, and supply are optimized by objective function.

  • Capacity of distributed energy system is designed by integrated optimization method.

  • Good energy efficiency, cost saving, and emission reduction are achieved.

Abstract

With the increase in the energy demands for heating, cooling, and electricity for buildings, the distributed energy system (DES), which is driven by renewable energy and natural gas, has become an economic and environmentally beneficial option for energy generation and supply. This paper presents a method for the integrated optimization of component capacity and annual operation of DES. In the outer cycle optimization, the capacity of energy generators and energy storages is optimized together to match the fluctuating energy demands of the whole year. In the inner cycle optimization, the hourly operation of energy generators and energy storage is managed following the operation strategy that reduces the exergy loss rate.

Case studies of DES design under different grid connection modes for an office building demonstrate the feasibility and flexibility of the proposed method. A traditional centralized system is employed as a baseline of performance, and two other capacity design methods are utilized for comparison. Results show that DES designed by the proposed method reduces carbon emissions by 0.170 kg/kWh, energy consumption by 0.392 kWh/kWh, and operation costs by 0.123 CNY/kWh, on average. In contrast to DESs designed by other methods, the performance of these parameters is improved by 0.019 kg/kWh, 0.078 kWh/kWh, and 0.031 CNY/kWh, respectively. Moreover, the DES design exhibits advantages in exergy saving, uncertainty resistance, and power grid independence.

Introduction

In recent times, the energy industry has begun moving away from carbon-based systems to more sustainable systems based on renewable and clean energy. Carbon emission reduction has become a new decisive factor that affects the operation and design of energy stations [1]. Although bioenergy could provide reliable and renewable energy in some regions [2], the uneven distribution and shortage of renewable energy leads to its scarcity in energy intensive business districts and industrial parks. Clean energy, such as natural gas and hydrogen, gradually have become the main energy sources of power stations [3]. Moreover, comprehensive utilization of energy sources is still an important way to improve the efficiency of energy utilization [4]. Combined cooling and heating power (CCHP) satisfies various energy demands by way of energy cascade utilization, which effectively improves energy recycling efficiency. Based on the energy conversion and supply mode of CCHP, the distributed energy system (DES) integrates various energy generators and multiple energy convertors in one energy station [5,6]. Differing from the traditional centralized system (TCS) that services numerous users through a large grid, DES produces and supplies energy on site according to demand [7]. In DES, the cogeneration of various energies and the consumption of both fluctuating renewable energy and stable clean energy provide economic and environmental benefits concurrently [8].

Various energy components can be selected for the optimal design of DES according to local resources and customer demands [9]. Elsied et al. integrated micro turbines, wind generators, fuel cells, and energy storage systems into a direct-current micro grid [10]. Famoso et al. used biomass from citrus processing plants for local cogeneration plants, and optimized the location, quantity, and performance of the proposed bioenergy system using a three-step model [11]. Ma et al. modeled the DES by integrating the cogeneration, photovoltaic (PV) array, and ground source heat pump for a large office building, which outperforms the separate system 17.85% energy saving, 55.50% cost saving and 34.22% carbon reduction [12]. Yang et al. introduced CCHP for hotel buildings and combined the gas turbine with solar thermal energy and compressed air energy storage, and energy efficiency increment gains 1.015% [13]. Yimen et al. designed a hybrid system equipped with a PV array, biogas generator, and wind turbine for a village [14]. Although various renewable energy components were used in the aforementioned DES, the main components with regard to CCHP and energy storage are the cogeneration unit, gas boiler, absorption chiller, electric storage and thermal storage. Considering the limited capacity of renewable energy, the optimization of the capacity and operation of these main components is the key to DES design [15]. However, the performance of many DESs is not as good as expected, which is mainly due to the unreliable capacity design method. Faced with the fluctuating renewable energy and customer demand, a DES with limited capacity has a higher dependence on the power grid and a high proportion of electricity purchase relative to the total energy supply. Also, a DES with excess capacity is often operated in low load rate, which decreases energy conversion efficiency. A reliable and accurate optimal design method of DES capacity is required to decrease the disparity between the designed capacity and the ideal capacity.

Capacity design aims to improve the system performance for annual operation on the premise of satisfying customer demand [16,17]. Because all components in the same energy flow cooperated during energy generation, the increase of the capacity or output of one component leads to the decrease of that of the others. Moreover, all energy flows are coupled for energy generation and supply, and hence, the change in the capacity in one energy flow affects the capacity in the other energy flows. In DES design, the component capacity and operation strategy influence each other. The system performance for annual operation is evaluated by hourly or daily operation, which is managed by the operation strategy. For the same capacity, different operation strategies result in different performances [18]. In the conventional operation strategy of cogeneration unit in the DES, two basic operation strategies are switched by the constraints. The strategies are following the electric load (FEL) and following the thermal load (FTL) [19]. For following the hybrid electric–thermal load (FHL) [20], the constraint is that neither electric generation nor thermal generation exceeds the demand. For following the maximum electric–thermal load (FML) [20], the constraint is that both electric generation and thermal generation satisfy the demand. For following the electric–thermal load of buildings (FLB) [21], the constraint is related to a parameter called the new load rate, which is the ratio of hourly electric demand to thermal demand. In addition, the electric cooling ratio (ECR) is the parameter considered in the operation strategy of electric and absorption chillers to manage cooling generation [22]. However, these constraints and parameters are not flexible and reliable enough to optimize the capacity. Faced with the fluctuating renewable energy and customer demand, the cooperation of DES components needs to be adjusted accordingly to improve the overall efficiency. The optional output of a cogeneration unit should not be limited to two points from FEL and FTL, which weakens its cooperation with other components, but should be the interval defined by these two points. Also, the values of these parameters should not be fixed, which results in low efficiency and energy waste on frequently, but rather should change according to the fluctuation of energy demands. Therefore, the hourly operation in capacity design should be optimized [23], and an integrated optimization method is required to include the optimal operation into the design of component capacity.

In previous studies on DES design, the improvement in energy efficiency, economy, and environmental friendliness is regarded as the optimization objective. Yousefi et al. considered the annual operating cost ratio, primary energy saving ratio, and carbon emission reduction ratio as the three objective functions for the optimal design of a hybrid CCHP system [24]. By adopting an analytic hierarchy process, the best system size was selected from the systems optimized by these three functions, and provided three benefits on 32.96%, 17.25% and 14.79%. Sameti and Haghighat analyzed the effects of storage on net-zero energy district systems by using Pareto optimal solutions [25]. The annual total cost and equivalent CO2 emission was reduced in the optimization model. In another study, the types, numbers, and sizes of energy devices in the DES were optimized with the weighted sum of the annual total cost and annual exergy efficiency [26]. Li et al. considered the performance factor indicator (PFI) as the objective function in the optimization model of both capacity design and operation optimization of the DES [27]. The PFI of separated system is 3.000, and that of the designed DES is decreased from 2.878 to 2.269. However, most of the annual objectives are not in line with the requirement of the optimization of hourly operation. For the annual performance, the inputted energy of the energy storage is almost equal to the outputted energy, and the difference between them has less influence on the total energy generation. Hence, the energy stored at the last hour could be ignored in the annual performance. With regard to the hourly performance, the situation is completely different. Because the energy storage cannot be charged and discharged in the same time, the operation status of the storage has a major influence on the hourly energy generation. Considering the efficiency curve of generators, the efficiency of energy generation will vary greatly with the operation status of the energy storage. In addition, energy generation in DES consumes multiple forms of energy with different exergy [26]. The energy saving or energy efficiency performance is not sufficient for the hourly optimization of energy cascade use. Therefore, the performance in terms of exergy loss rate is proposed in this research and regarded as the optimization objective of the hourly operation of the DES.

Owing to the inflexible operation strategy and the unsuitable optimization objectives, reliable and accurate optimal design methods of DES capacity are still lacking, such that the DES capacity mismatches with customer requirements and the performance of energy supply is lower than expected. This paper presents the objective function of reducing exergy loss rate to optimize the hourly output of each component and to replace the traditional operation strategy in the capacity design of DES. The main novelty and contributions of this study are as follows:

  • Exergy loss rate is proposed as the objective to optimize the hourly off-design output of DES components. Constraints of the objective function narrow the optimization range of energy cascade utilization represented by exergy density. As a result, the flexibility and performance of DES operations are significantly improved.

  • An integrated optimization method is modified to optimize component capacity in the outer cycle and to enhance annual performance in the inner cycle. The objective function is solved every hour to obtain the annual operation of DES. As such, the reliability and accuracy of the optimized capacity are significantly improved.

The remainder of the paper is organized as follows. Section 2 describes the exergy calculation in an office DES and the objective function of exergy loss rate in hourly optimization of DES operation. The framework, algorithms, and evaluation criteria for the integrated optimization method of capacity design are presented in Section 3. In Section 4, case studies of DES design with different design methods and grid connection modes are described. Section 5 analyzes and discusses the result of case studies and the feasibility of the novel method. The conclusions are summarized in Section 6.

Section snippets

DES description

As an energy station located at the customer side, the DES has three connection modes with the power grid, namely, the bidirectional connection mode (BCM), unidirectional connection mode (UCM), and disconnection mode (island mode, DCM) [28]. Fig. 1 describes a traditional DES servicing an office building, which consists of the following components: the PV array, a micro wind turbine, a cogeneration unit, a natural gas boiler, an electric chiller, an absorption chiller, electric storage, and

Framework of the integrated optimization

An integrated optimization method for the capacity design of the DES is proposed in this research. This method integrally optimizes the component capacity and annual operation by the outer cycle and inner cycle [36, [37], 38, [39]]. The framework is shown in Fig. 4.

For the outer cycle, the system structure, design objective, evaluation method, and optimization algorithm are defined for the optimization of capacity. The initial capacity is created, evaluated, and modified to achieve the optimal

Case study

The proposed operation strategy and optimization method were applied to case studies to obtain the optimal capacity and performance of DES. In the case study, performance of TCS was evaluated as the baseline. Then, for different grid connection modes, the capacity of the DES was designed by three methods: the conventional design method with FLB operation strategy; the integrated optimization method with PFI operation strategy; and the integrated optimization method with EXR operation strategy.

Annual performance analysis

The annual performance for each case is presented in Table 8. Considering the energy saving, economy, and environmental indicators, the performance of the DES designed by the EXR method (case 3, case 6, and case 9) is better than that of the DES designed by the PFI method (case 2, case 5, and case 8); meanwhile, the PFI method resulted in better performance than that achieved by adopting the FLB method (case 1, case 4, and case 7).

The average benefits of the DES designed by each method, in

Conclusions

The energy efficiency, economy, and environment friendliness of the DES arise from the cooperated operation of its components. Both component capacity and operation strategy affect the annual and hourly performances of the DES. In the capacity design of the DES, the conventional operation strategy leads to the disharmony in terms of component capacities and incorrect estimation of operation performance. In addition, optimization objectives directly affect both the hourly and annual performance

Abbreviation

    ACEF

    annual carbon emission factor

    AECR

    annual energy consumption rate

    AEGC

    annual energy generation cost

    BCM

    bidirectional connection mode

    CCHP

    combined cooling heating and power

    DCM

    disconnection mode

    DES

    integrated energy system

    EA

    evolution algorithm

    ECR

    electric cooling ratio

    EXR

    exergy loss reduction

    FEL

    following the electric load

    FHL

    following the hybrid electric–thermal load

    FLB

    following the electric–thermal load of buildings

    FML

    following the maximum electric–thermal load

    PFI

    performance factor indicator

    FTL

Funding

This work was supported by the National Natural Science Foundation of China [grant numbers 61821004, 61733010], Department of Science and Technology of Shandong Province (grant number 2019JZZY010901), Natural Science Foundation of Shandong Province (grant number ZR2019ZD09), Innovation Team Project of Jinan Science and Technology Bureau (grant number 2019GXRC003), and the Young Scholars Program of Shandong University (grant number 2016WLJH29).

CRediT authorship contribution statement

Haoran Li: Conceptualization, Methodology, Software, Data curation, Writing - original draft, Visualization, Investigation. Bo Sun: Investigation, Writing - review & editing. Chenghui Zhang: Conceptualization, Investigation, Supervision.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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