1. Introduction
The share of Renewable Energy Sources (RES) in energy systems is growing rapidly to accelerate the energy transition and tackle climate change. However, their penetration in the heating and cooling sector, which accounts for more than 50% of the final energy demand in EU [
1], is only about 22% in Europe [
2]. One of the key solutions to increase this share on the short term and in a cost-effective way is to integrate renewable energy in existing District Heating (DH) systems. Many initiatives are currently taken to do so. According to IRENA [
3], Denmark has the ambition to increase the share of RES in their DH systems up to 73% in 2030 (vs. 42% in 2014). China targets a 24% share by 2030.
In terms of renewable resources, biomass, solar heating and geothermal energy are the options with largest potential to reach higher shares of RES in DH’s [
3], also at large scale [
4,
5,
6]. Among these options, solar collectors present the additional challenge of being an intermittent source of energy, therefore potentially requiring additional heat storage, which represents an interesting optimization problem.
Hot water Thermal Energy Storage (TES) can provide load shifting and is 100 times cheaper than electricity storage for the same energy capacity [
7]. Thanks to TES, up to 25% RES would be integrated in energy systems without significantly affecting its efficiency [
8]. Hybrid systems composed of Combined Heat and Power (CHP) units integrated with RES and TES is therefore considered as a first step towards the 4th generation district heating systems [
8].
TES alone can also be added to existing CHP’s. It contributes to peak load shaving, it can store energy when the demand is low and deliver it when the demand is high. With an optimal operation, it can also increase the energy efficiency, which in turn results in lower CO
2 emissions [
9]. In many studies, the optimization of a combined CHP-TES system often focuses on economic aspects, taking into account the cost of CO
2 emissions. Benalcazar [
10] proposed an optimization method based on the economic performance for the optimal sizing of how water TES, integrated in an existing CHP plant considering specific investment cost and different carbon prices. His analysis showed that the integration of the TES units can save operational cost and decrease the use of the heat-only boiler, which reduces fuel consumption and decreases CO
2 emissions. Mugnini et al. [
11] assessed possible energy flexibility strategies to improve the performance of such system. Their results revealed that a hot water tank can increase the CHP working hours and primary energy savings. Lai et al. [
12] developed an operation optimization model based on Particle swarm optimization method, to investigate the flexibility and thermodynamic performance of a CHP unit integrated with an integrated heat storage tank. Their results show that such an integration led to an increased range of operational conditions of CHP units.
Although the addition of Solar Thermal Collectors (STC) can lead to larger CO
2 emission savings than TES alone, they generally increase the production costs of existing systems [
3]. The question of the optimal combination of STC and TES in terms of both economic and environmental impacts may therefore be raised. This corresponds to a multi-objective optimization problem with two design variables, i.e., the sizes of the STC and the TES systems.
Single economic objective optimization does not provide alternative solutions to deal with conflicting objectives [
13]. Therefore, recent research efforts focused on multi-objective optimization of energy systems. Multi-objective optimization is used to find a trade-off between two or more conflicting objectives to support decision making. Ren et al. [
14] proposed a multi-objective linear programming method for operational strategy of a Distributed Energy System (DES). Their model was based on trade-off analysis of economic and environmental optimization. Fazlollahi et al. [
15] developed a multi-objective, multi-period optimization for sizing and operating a DH system with the objectives of maximizing the system efficiency and minimizing the CO
2 emissions and the Annual Total Cost (
ATC), the annualized value of the total cost over the lifetime of the project. Luo et al. [
16] developed a framework for the optimization of DES integrated with Genetic Algorithm for multi-objective optimization, and multi-criteria evaluated by Technique for Order Performance by Similarity to an Ideal Solution (TOPSIS) method. Karmellos et al. [
17] presented a multi-objective Mixed-integer linear programming (MILP) model for the optimal design and operation of DES by ε-constraint method, with minimizing the
ATC, and the total carbon emission as objective function. Franco and Versace [
18] carried out a multi-objective strategy considering energetic and economic objectives to investigate design and operation strategy of a CHP-TES to DH network.
As discussed above, various approaches have therefore been applied to determine the economically and/or environmentally optimal design and operation of RES and TES integrated into existing CHP systems. However, the proposed methodologies can be further improved in the following respects. First, the modelling of the CHP systems could be more accurate. An increased accuracy of the techno-economic models for the following aspects could lead to more accurate results [
19]: Piece-wise Linear Investment functions allowing for a non-linear evolution of the investment costs, and account for part-load efficiencies, start-up costs, CHP acceptable operation ranges and maximum ramp rates, which significantly affect technical and economic performances. Secondly, few recent works [
15,
16] integrated multi-objective optimization models with decision making methods to optimize the capacity and the operation strategy of TES and RES integrated to existing CHP systems. Moreover, limited research was carried out on the effect of fluctuating investment cost on the sizing and the operation of the system.
In this work, we therefore aim at integrating multi-objective optimization and decision-making methods featuring advanced techno-economic models, and to apply them to 1the STC and TES systems.
Our main objectives are the development a comprehensive methodology to allow decision-makers to determine the optimal design of hybrid heat and power production systems and to assess the economic and environmental impact of the optimal integration of STC and TES systems into existing, conventional CHP systems.
The main innovative features of this work can be summarized as follows:
The techno-economic models of the sub-systems features Piece-Wise Linear Investment function, part-load efficiencies, start-up costs, maximum ramp rates and CHP acceptable operation ranges.
The variation of the economic and technical parameters, such as ambient temperature, electricity and fuel prices, is considered.
Pareto-optimal solutions are generated using multi-objective optimization, from which the optimal solution is picked using the TOPSIS-entropy method, an effective method to make decisions processes more reliable and accurate.
The paper is organized as follows.
Section 2 describes the proposed methodologies. The case of a hybrid energy system is defined in
Section 3, including the input data and the investigated scenarios.
Section 4 presents and discusses the results of the case study. Lastly, conclusions are drawn in
Section 5.
5. Conclusions and Future Work
A framework for the multi-objective optimization of the integration of Solar Thermal Collectors (STC) and Thermal Energy Storage (TES) systems in existing fossil-fuel based heat and power production systems was presented. The proposed method was applied to the representative case of a medium-scale CHP system coupled to a District Heating network. As a comparison, the integration of TES or STC alone was also considered.
The proposed TOPSIS-entropy method has been proved to be efficient to select the optimal design in terms of trade-off between cost and CO2 reduction. Our results show that, while the addition of TES or STC alone results in limited economic and environmental performances and exhibits a rapid increase of the cost with the targeted CO2 emission reduction, the optimal combination of TES and STC can lead to a reduction of both the cost and the CO2 emissions: respectively 3% and 10% in the studied case. For larger CO2 emissions savings, beyond the optimal trade-off, the additional cost remains limited compared to the other solutions. The integration of TES and STC also significantly improves the system flexibility and efficiency. TES allows for peak load shaving and load valley filling, resulting in up to 52% less operation of peak units. For the optimal design, the total efficiency of the system increases from 87% to 92%. The share of renewable energy reaches 10% when both TES and STC are integrated, compared to 7% with STC alone.
The operational flexibility of the CHP unit itself is also increased by the integration of TES and STC, which helps increasing the electrical power generation.
A sensitivity analysis shows that only the heat demand has a significant impact on the environmental performance, while both the heat demand and the fuel price have a significant influence on the economic performances. Furthermore, the optimal TES volume is less sensitive to the uncertainties on the inputs than the STC surface, that is more impacted by the economic parameters.
In future works, the same methodology could be applied to the integration of more renewable energy sources such as heat pumps and power-to-X based on renewable electricity production. The case of a new CHP unit will also be investigated instead of a retrofit, taking into account the optimal sizing of this unit and the related investment costs. Moreover, the impact of the uncertainties on the input data could be studied using advanced Uncertainty Quantification and Robust Design techniques.