1. Introduction
In recent years, with the increasing attention on global climate change and sustainable development, the penetration of renewable energy resources (RES) has steadily grown in the global electricity market. As renewable energy sources are starting to play a prominent role in revolutionizing modern power systems, the impact on their operation and reliability no longer goes unnoticed and neglected. However, because of their variability and difficult-to-predict nature, renewable resources are always considered an unreliable resource, and their scheduled generation cannot be ensured [
1,
2]. Therefore, it seems that after overcoming the impediments related to the cost, the next problem which should be solved is the reliable and economically justifiable integration of RES into the power system. This is especially important in the case of RES such as wind and solar, which tend to exhibit a significant temporal and spatial variability [
3]. The problems of RES integration into the power system have been studied in several studies [
4,
5,
6].
In order to provide stable generation, coordination of several kinds of energy sources may be an effective way to overcome these disadvantages above. Because of the large scale and good regulation performance, hydroelectric power can effectively restrain the fluctuations in wind and photovoltaic generation to improve their stabilities. Based on integrated technology, establishing a hydro–photovoltaic–wind hybrid system is seen as a promising method to realize the conception [
7,
8]. Malakar et al. [
9] performed the coordinating strategy of a wind–hydro hybrid system connected grid under frequency-based pricing. Reference [
10] studied the portfolios of multiple energy sources considering the complementarity between wind power and photovoltaics. The portfolios may play an important role in reducing the fluctuations and intermittency to improve the reliability of individual generation [
11]. A method to solve the optimal power flow problem with different probability density functions for wind and solar power was suggested by Reddy [
12]. A stochastic day-ahead optimal strategy for a wind–hydro system was presented by Biswas [
13] considering the risk of the system. References [
14,
15,
16,
17] studied the models of the wind–hydro hybrid system to reduce the cost of imbalances. Compared with renewable resources, combined heat and power (CHP) systems are highly controllable and have quite quick rates. Therefore, these systems with CHP are flexible and can be used to ensure balance and improve the stability of RES integration into the power system [
15]. The models for determining the strategy for optimal operation and trading of CHP systems have many relevant studies. CHP systems can be optimized based on different optimization criteria, such as energy savings, cost reduction, minimum environmental impact, or a combination of all of these [
16]. Several methods and criteria have been proposed in the literature for optimization of the size and operation of CHP systems [
17,
18,
19,
20,
21]. References [
22,
23] proposed deterministic optimization models, while References [
24,
25,
26,
27] presented stochastic programming models of CHP systems due to their ability to approach various uncertainties in CHP system operation. References [
28,
29] proposed an economic dispatch model that included CHP units, with a comprehensive survey reported [
30,
31,
32]. Lai [
33] designed a CHP system by integrating the thermal storage techniques considering the uncertainty of demand. Taking the variation of demands and prices into account, Carpaneto et al. [
34] solved the best CHP plan based on the decision theory concepts. Zapata et al. [
35] promoted an aggregation model of a CHP–photovoltaic (PV) hybrid system under uncertainty in the Belgian market. In addition, due to the ability of energy storage, the battery energy storage system (BESS) is generally regarded as an effective tool to deal with the intermittent characteristics of RES. Liao [
36] proposed an optimization method for sizing and scheduling BESS and the smart inverter (SI) of a photovoltaic (PV) system to ensure the PV system owner’s investment returns and to assist the distribution system operator (DSO) in adjusting the voltages. Chettibi [
37] proposed an intelligent control strategy for a grid connected hybrid energy generation system consisting of photovoltaic (PV) panels, fuel cell (FC) stack, and BESS. Branco [
38] put forward the integration of RES considering the installation of a battery energy storage system (BESS) into an isolated power grid to keep the costs down.
However, to our knowledge, the concept of coupling all the above energy together has not been proposed in the existing literature. In this paper, we coupled the photovoltaic modules (PV), a wind turbine (WT), battery energy storage modules (BESS), electric vehicle chargers (EV), CHP, and a hydroelectric power plant as a portfolio, in an attempt to reduce the imbalance and ultimately minimize the cost of the portfolio considering risk factor. Based on the perspective of the concept, a renewable-based hybrid energy system is proposed in the paper, and an optimal scheduling model of this system to minimize the cost of operation and risk is put forward considering multiple uncertainties, which include renewable resource volatility, the load demand, and different energy service providers’ coefficients of risk aversion. To handle this complex optimization problem, a method combining the Latin hypercube sampling, scene reduction, and piecewise linearization is proposed. A large number of samples were generated with the Latin hypercube sampling method according to the uncertainties, including the renewable resources availability, the load demand, and the risk aversion coefficients, and the generated samples were reduced with the scene reduction method. Additionally, the piecewise linearization method was applied to convert nonlinear constraints into linear to obtain the best results of each scene.
In summary, the main contributions of this paper can be listed as below:
- (1)
Propose a concept of a renewable-based hybrid energy system along with a corresponding mathematical model which can be used to simulate and optimize its performance.
- (2)
Introduce an optimal optimization model which focuses on minimizing the operating cost of energy service providers considering the environment as much as possible.
- (3)
Investigate the distinction of uncertainty variables volatility on the energy exchange with the power grid.
- (4)
Research the impact of different risk aversion coefficients on the operation of energy service providers.
The remainder of this paper is organized as follows.
Section 2 formulates the mathematical model for the operation of the hybrid power system. In
Section 3, the solution method is described in detail. In
Section 4, numerical simulations of the proposed model are applied to a test system on real-life data, and discussions are provided accordingly. Finally, the conclusions of the paper are drawn in
Section 5.
5. Discussion
In order to show the advantages of the proposed model, the tie-line power performance and the comparison results of the hybrid energy system with different situations are given in the following part, in which the effectiveness and the economy improvement are verified.
5.1. Comparison of Different Models
Considering the high cost of investment about BESS and charging piles, we compared the optimal results without the BESS or charging piles. The results of four different models are shown in
Table 9. The tie-line power with power grid is represented in
Figure 5.
BESS has the ability to store energy, so it can discharge at high prices and charge at a low price to reduce the economic cost and stabilize the tie-line power. Comparing scheduling models where BESS participates and where it does not, we found that the total cost and risk all dropped, and the economic objective was lower in
Table 9, which is consistent with the fact. Regarding the hybrid energy system, the charging piles of EV are opposite to the BESS. The charging piles can be seen as a controllable load, so the total cost and risk will drop without them.
Figure 5 shows the tie-line power curve of the compared model. Throughout the time of operation, the curve of the model including all is much smoother than the compared model without BESS and EV. At periods 28 to 42, 46 to 59, and 70 to 76, the power of the tie-line in the proposed model is lower than those of the compared models without BESS and EV, respectively. Therefore, with the diversification of load demands, expanding the types of energy is important to peak shaving and valley filling.
In order to show the advantages of the proposed method, the computational time and the comparison results of the hybrid energy system with different situations are given in the following part.
5.2. Comparison of Different Methods
It is important to compare the proposed method with traditional methods regarding the above model, including all energy modules. This paper illustrates and compares the following three cases to investigate the advantages of the proposed method. Case 1 is the method proposed in this paper, and case 2 is the method with Monte Carlo sampling, scene reduction, and piecewise linearization. Case 3 is the intelligent method (PSO). The results of the three different methods are shown in
Table 10.
The method in case 2 is similar to the proposed method in case 1, and the difference is the sampling method. In
Table 10, the results of the objective and computational time in case 2 is slightly more than those in case 1. However, the computational time of the intelligent method (PSO) is much longer than that in case 1. The computational time in case 3 is nearly four times as long as that in case 1. The main reason is that piecewise linearization can effectively reduce the solving time. The objective result in case 3 is slightly equal to case 2. From
Table 10, the proposed method has advantages in optimal results and computational time.
5.3. Impact of the Fluctuation of Uncertainty Variables
According to different seasons and regions, the difference of load curve and renewable resources is obvious. Therefore, it is necessary to consider the fluctuation of uncertainty variables on the influence of the objective function. Considering three scenarios: Reduction of 10%, unchanged, and increase of 10%, the optimal results are shown as follows:
Table 11 represents the effects of the fluctuation of uncertainty variables on the objective function of the proposed model in hybrid energy system. Wind and PV as renewable resources have the features of low cost and high risk. With the increasing output of wind or PV, the cost reduces from 111,275 to 111,114 and 111,135, while the risk increases from 120,306 to 120,471 and 120,429 in
Table 11. The results are opposite as to the case of decreasing output. In addition, the fluctuation of local load results has much more of an impact on the optimal results. The relative change reaches 20% when the load fluctuates by 10%, while the relative change only reaches 1% when the renewable resources fluctuate by 10%. Therefore, it is particularly important to improve the forecasting accuracy of the load demand.
5.4. Impact of the Time Intervals of RES
According to different granularity requirements, the difference in time intervals of RES is obvious. Therefore, it is necessary to consider the different granularity requirements of RES on the influence of the objective function. Considering three scenarios: 5-min intervals, 10-min intervals, and 15-min intervals, the optimal results are shown as follows:
Table 12 represents the sensitive studies of different time intervals of RES. Due to the small scale of RES compared to the whole scale of energy system, the impact of different time intervals on the optimal results can be negligible. The objective results of different scenarios are approximately equal.
5.5. Impact of the Coefficient of Risk Aversion
In the renewable-based hybrid energy system supplied by energy service providers, different providers have different coefficients of risk aversion. Consider five scenarios to study the impact of risk coefficient for optimization model. The optimal results are shown in
Table 13.
Table 13 represents the sensitive studies on the provider’s risk aversion for the hybrid energy system. A larger coefficient of risk aversion for the energy service provider indicates less tolerance towards possible uncertainty in the model. With the increase of the coefficient of risk aversion, the system pays more attention to risk, leading to the increase in the cost of risk. Thus, the hybrid system’s objective cost is increased by the increase in coefficient of risk aversion.