# Regional Renewable Energy Installation Optimization Strategies with Renewable Portfolio Standards in China

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## Abstract

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## 1. Introduction

## 2. Literature Review

_{2}emissions by 34% through optimized selection and operation of energy technologies. Fan et al. [20] established a comprehensive planning model composed of multiple regression and linear programming models based on the proposed 2020 provincial renewable energy portfolio standard. Through the inter-provincial allocation of renewable energy and the combined renewable energy portfolio standard and green certificate trading system, they promoted renewable energy resource configuration optimization. One of the ways to improve power reliability is to combine multiple renewable energy and storage systems. Memon and Patel [21] provide a comprehensive overview of the different scale and optimization methods developed by the research community. Considering the availability of renewable energy resources, Makhloufi et al. [22] adopted the multi-objective cuckoo search algorithm to solve the multi-optimization problem of energy strategies. Deveci and Güler [23] proposed a two-step multi-objective optimization framework for renewable energy planning, taking into account different situations of renewable energy investment expenditure and optimal use of resource availability. When developing sustainable local energy systems, Hori et al. [24] visualized pareto solutions for the optimal renewable energy mix using an environmentally sustainable renewable energy region optimization utility with a multi-objective evolutionary algorithm. The use of hybrid renewable energy systems holds great promise for sustainable electrification and supporting countries to achieve their energy access goals (Elkadeem et al. [25]). The use of optimized multi-energy systems, including renewable energy, cogeneration, and energy storage, has been shown to be effective in reducing carbon emissions (Martelli et al. [26]). Liu et al. [27] improved the planning of distributed energy systems by adopting an integrated optimization method that considered equipment configuration and operation strategy. Appropriately optimized control methods are important for ensuring efficient, safe, and high-quality power transmission (Hannan et al. [28]). Regional energy systems are based on the advantages of different types of energy. Lei et al. [29] construct an economic and efficient regionally integrated energy system for scenario tree path optimization considering long-term multiple uncertainties. Sobhani et al. [30] proposed a future-oriented optimization approach for renewable energy to adapt to climate change and energy price changes. The optimal design of a regionally integrated energy system is necessary to realize the effective use of various energy resources and improve energy efficiency and economic benefits. Li et al. [31], based on typical residential and commercial modules constructed on actual regional blocks, studied the capacity allocation optimization of integrated energy and configured multi-energy systems and optimized real-time calculation results. Zhou and Zhou [32] proposed an integrated imprecise optimization framework and discussed their potential applications in hybrid renewable energy systems. The main challenges of implementing renewable energy systems include geographic constraints on resources and technologies, increased energy and material demand, and the need to reduce emissions while remaining cost-sensitive. Kakodkar et al. [33] analyzed the conversion of renewable energy systems and reviewed optimization methods to provide a reference for addressing the challenges.

## 3. Regional Power Installation Optimization Model

#### 3.1. Symbol Definition (Nomenclature)

#### 3.1.1. Subscript

#### 3.1.2. Parameters

#### 3.1.3. Variables

#### 3.2. Objective Function

#### 3.3. Constraints

#### 3.3.1. RE Resource Potential Constraint

#### 3.3.2. Regional RPS Target Constraint

#### 3.3.3. Constraints on Inter-Regional Transmission

#### 3.3.4. Flexibility Constraint

## 4. Algorithm Design

**Step 1**Chromosome Coding. The real number coding method, including multiple regional power grids and multiple power types, is used, and the cell structure storage is adopted. The storage structure is $m\times n$. Chromosomes are expressed as:

**Step 2**Initial Population and Fitness Calculation. To improve the global search performance and the quality of the solution of the genetic algorithm, the initial random number is transformed to make it between 0 and the maximum potential of the installed capacity, and the chromosomes satisfying the constraint Equations (7), (9) and (10), which are retained as the initial population. Each chromosome is brought into the objective function, and $f(x)=1/TC$ is defined as the fitness function to place the dominant individual in the dominant set.

**Step 3**Choice. The new species group is selected by the roulette method, and the dominant individuals obtained in step 3 are added to the new species group to improve the population quality.

**Step 4**Crossing. According to the crossover probability, the parents participating in the crossover operation are selected from the new species group, and they are randomly paired to cross to produce offspring. Due to the exchange of some genes in the cross operation, it is difficult for some offspring to meet the constraints. Therefore, it is necessary to conduct a feasibility test on the chromosomes of the offspring, that is, to test whether each gene meets the constraint Equations (7)–(10).

**Step 5**Variation. To improve the population diversity, chromosomes were randomly selected from the new species group by mutation probability. The specific operations are as follows: (1) Randomly select the genes used for mutation operation and exchange them, and (2) Carry out the feasibility test on the variation, i.e., check whether each gene meets the constraint Equations (7), (9) and (10).

**Step 6**Termination Discrimination. If the algorithm does not reach the maximum number of iterations, return to step 3. Otherwise, enter the stage of advantage set selection.

**Step 7**Advantage Set Selection. Through the calculation of applicability, the dominant individuals of various groups are retained to form a dominant set. After the termination of the algorithm, the dominant set is screened to obtain the optimal individual.

#### Algorithm Performance Analysis

## 5. Data Sources and Scenarios Setting

#### 5.1. Scenario Design

#### 5.2. Data Sources

## 6. Results and Discussion

#### 6.1. Regional Renewable Energy Installation Optimization Results

#### 6.2. Comparative Analysis of Different Scenarios

- (1)
- By the end of 2030, under the pessimistic scenario, in which the pandemic cannot be effectively controlled, the newly installed capacity of non-hydropower RE power generation will be about 1655.54 GW, of which the largest newly installed generation capacity of wind power in the North China Power Grid will be about 150.14 GW. The region with the largest newly installed capacity of solar and biomass power generation will be the Northwest Power Grid, 429.98 and 6.93 GW, respectively.
- (2)
- Under the neutral scenario from 2021 to 2030, the total newly installed capacity of non-hydropower RE power generation will be about 1661.01 GW, of which the newly installed capacity of wind power generation in the North China Power Grid will be the largest at about 150.62 GW. The top-two regions with newly installed solar power generation capacity are the Northwest and North China Power Grids, with new capacities of 431.57 and 253.52 GW, respectively. Regarding biomass power generation, the Northwest Power Grid will add 6.96 GW of installed capacity.
- (3)
- Under the optimistic scenario, in which the pandemic has been effectively controlled, the newly installed capacity of non-hydropower RE power generation during the study period will be about 1667.39 GW, and the total installed capacity will be at least 18.78 GW more than that under the pessimistic scenario. As can be seen from Figure 6, the region with the largest newly installed wind power generation capacity is the North China Power Grid, while the region with the largest newly installed solar and biomass power generation capacity is still the Northwest.

## 7. Conclusions

- (1)
- Under the RPS system, the newly installed capacity of renewable energy will be mainly non-hydropower. By 2030, China’s installed capacity of non-hydropower renewable energy will reach 2.2 TW. Therefore, we should make full use of existing resources, explore new and clean energy, and decompose and arrange indicators for each province and city according to the allocation results of regional renewable energy, which can further optimize the energy layout of each region.
- (2)
- The results of the optimal allocation model established in this paper show that in non-hydropower renewable energy, the installed capacity of wind power generation accounts for about 31.9% of total installed non-hydropower RE capacity, and the capacity of solar power generation accounts for 65.7% of total installed capacity. The implementation of the RPS policy is to encourage the redevelopment of renewable energy in various regions. Therefore, the effects of implementing it can be determined by comparing the installed power generation capacity under different scenarios of the pandemic.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

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Algorithm | Target Mean Value | Optimal Value | Worst Value | Average Deviation |
---|---|---|---|---|

IGA | 0.278 | 0.214 | 0.354 | 0.020 |

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**MDPI and ACS Style**

He, Y.; Wan, L.; Zhang, M.; Zhao, H.
Regional Renewable Energy Installation Optimization Strategies with Renewable Portfolio Standards in China. *Sustainability* **2022**, *14*, 10498.
https://doi.org/10.3390/su141710498

**AMA Style**

He Y, Wan L, Zhang M, Zhao H.
Regional Renewable Energy Installation Optimization Strategies with Renewable Portfolio Standards in China. *Sustainability*. 2022; 14(17):10498.
https://doi.org/10.3390/su141710498

**Chicago/Turabian Style**

He, Yuanyuan, Luxin Wan, Manli Zhang, and Huijuan Zhao.
2022. "Regional Renewable Energy Installation Optimization Strategies with Renewable Portfolio Standards in China" *Sustainability* 14, no. 17: 10498.
https://doi.org/10.3390/su141710498