1. Introduction
Energy and environmental issues are key challenges for future global sustainable development [
1,
2]. Combined cooling, heating and power (CCHP) systems, with the environmental benefits based on their use of waste heat recovery technology and the energy cascade utilization principle, which have attracted considerable research attention. A CCHP system generates both electricity and useful cooling/heating energy to convert 75–80% of a fuel source into usable energy. Renewable energy sources (RESs) are expected to account for increased energy consumption due to significant environmental benefits, and can be better used in conjunction with CCHP systems [
3]. The efficient operation of the system depends on the energy supply and demand matching, but the variation of load in actual operation will cause the mismatch between the energy supply and demand [
4].
Thermal energy storage (TES) units are an effective method for CCHP systems to reduce the mismatch between the energy supply and demand, and improve the energetic and economic performances [
4,
5]. Xu et al. studied a smart building energy system comprising a CCHP system, RESs, and various energy storage devices, taking into account the uncertainty of demand and solar radiation [
6]. Liu et al. proposed a CCHP system with TES as an energy station [
7]. Mohammadkhani et al. proposed a residential microgrid equipped by CCHP with considering electrical energy storage and thermal energy storage [
8].
Demand response (DR) is also an effective way to deal with source-load mismatch and improve operation efficiency, replacing energy storage to a certain extent and reducing installation costs. DR is defined as “changes in electric usage by end-use customers from their normal consumption patterns in response to changes in the price of electricity over time, or to incentive payments designed to induce lower electricity use at times of high wholesale market prices or when system reliability is jeopardized” [
9]. Aalami et al. proposed an incentive-based DR model considering interruptible/ curtailable loads, using the price elasticity of demand and the customer benefit function, which improved the characteristics of the load curve and was also welcomed by customers [
10]. Ghazvini et al. verified the performance of an intelligent home energy management system algorithm to schedule the consumption of controllable devices in a smart household [
11]. Zakariazadeh et al. demonstrated that the adoption of DR programs can reduce total operation costs and improve energy efficiency [
12].
The operation strategy is a key factor that determines the success of application, energy performance, and environmental benefits of the CCHP system [
13,
14], especially for efficient operation of the system with TES and DR [
15,
16]. However, the basic operation mode, including following thermal load (FTL) and following electric load (FEL), has limited ability to optimize the system operation. Wang et al. [
17] proposed an improved FEL operation strategy and Feng et al. [
18] proposed an improved hybrid load tracking method (FHL) based on a comparison between FEL and FTL strategies. Ma et al. developed a seasonal operation strategy for a new distributed energy system integrating CCHP, photovoltaics, and a ground source heat pump (GSHP), which improved the system performance index [
19]. Cao et al. developed a configuration optimization framework that extends existing energy system optimization studies in following four aspects: complete system optimization from the beginning, comprehensive energy conversion equipment modeling, modeling of cascaded configurations, and consideration of transient loads and weather profiles [
20]. Luo et al. proposed a new two-stage coordinated control method for managing the energy of the CCHP micro-grid, including an economic dispatch stage (EDS) and a real-time adjustment stage (RTAS), and conducted simulations to verify the performance of the method [
21]. Afzali et al. divided the operation of the CCHP system into three cases according to the capacity of the gas turbine, thermal load, and electrical load and gave the analytical expression for the ratio of electricity price to natural gas price [
22]. Li et al. proposed a hybrid optimization method that combines the GA and dynamic programming (DP) to obtain the optimal scheduling scheme for CCHP system with TES [
23]. Deng et al. proposed an optimal scheduling strategy based on actual operation of an energy station with energy storage and a GSHP to minimize daily operating costs [
24]. Zheng et al. proposed a new thermal storage strategy (TSS), which determined the operating state of the PGU according to the power demand, thermal demand, and state of the thermal storage device. Results show that the TSS improves the performance of CCHP systems compared to traditional strategies [
25]. Kuang et al. proposed a dynamic optimization method for CCHP systems with energy storage, and obtained the most economical operation scheme in a very short time by adopting a dynamic solution framework [
26].
In summary, most research has focused on optimizing the energy supply and storage strategies to improve the system performance index and meet users’ demands. A small number of studies have focused only on DR program, which mainly involving the use of time-of-use electricity prices to achieve the electricity load transfer. Few studies have carried out the management of cooling and heating load. In fact, cooling and heating load account for 70–80% of the building load. Load demand side management can coordinate the adjustment of cooling and heating, without additional equipment to reduce the mismatch between the energy supply and demand.
This paper proposes a collaborative optimization scheduling strategy for a multi-energy complementary CCHP system consisting of solar photovoltaics (PVs), wind turbines (WTs), a power generation unit (PGU), a heat pump (HP), an absorption chiller (AC). The system uses schedulable loads, including schedulable cooling, heating, and electrical loads, instead of energy storage. The scheduling of load demand and energy supply is unified into a collaborative optimization model. Comprehensive evaluation indexes of the economy, environment, and energy performance are chosen as the optimization objectives, and GA is used to solve the optimization problem. Case studies are conducted on a residential building to verify the effectiveness of the proposed approach. The main contributions of this paper are as follows:
A schedulable cooling and heating load model is established using the indoor and outdoor temperature relationships of a house, and the demand side management for multiple types of loads was achieved.
The system energy supply and load demand are included in the unified optimization scheduling framework, and a multi-objective collaborative optimization scheduling model is established to achieve global optimization of day-ahead scheduling.
Compared with the RESs CCHP system containing energy storage and DR, the proposed method achieves better performance indexes and reduces system complexity.
The remainder of this paper is organized as follows:
Section 2 details the system structure and energy flow.
Section 3 presents the optimal scheduling model and solution method. The case studies and results are discussed in
Section 4. Finally,
Section 5 presents the main conclusions.
5. Conclusions
This paper presents a multi-energy complementary CCHP system that combines RESs and schedulable loads. A gas-fired internal combustion engine generator set, PV panels, WTs, and the utility grid comprise the energy side of the system, while a collaborative optimal scheduling model that coordinates multiple energy sources, CCHP, and schedulable loads was proposed to simultaneously satisfy the demand for cooling, heating, and electricity. The system uses schedulable loads instead of energy storage, at the same time, smart residential appliances were introduced as the schedulable electrical loads to implement the DR program. Models for schedulable cooling and heating loads based on variable temperatures were then established. Furthermore, a multi-objective optimization method was established to determine the trade-off between the PESR, ERR, and OCSR performance indexes. Finally, a few cases were used to illustrate the feasibility and superiority of the proposed approach compared to the existing approaches. The major findings are summarized as follows:
- (1)
The collaborative optimal scheduling model can coordinate multiple energy sources, CCHP, and schedulable loads more efficiently than other models.
- (2)
Compared to the system using TES and DR, the PESR, ERR, and OCSR values of the proposed method are 7.44%, 6.59%, and 4.73% higher, respectively, on a typical summer day.
- (3)
The proposed approach also simplifies the system structure and reduces the mismatch between the energy supply and demand.