Abstract:

In response to the practical issue of how to assess the photovoltaic(PV) systems from multi-perspectives and assist the green and low-carbon development of China’s PV industry under the background of “carbon peak and carbon neutrality”, a data-driven decision-making model for PV systems is proposed under the dimension of sustainable development assessment. First, the comprehensive assessment criteria system of PV system is established based on the sustainable assessment dimension. Next, to address the uncertainty and volatility in the performance level of PV systems, interval-based granules, and interval type-2 fuzzy sets (IT2FSs) are constructed to quantize the information. Then, to address the preferences of unknown weights of PV system evaluation criteria, the Monte Carlo method is used to generate criteria weight space, and the stochastic multi-criteria acceptability analysis(SMAA) is applied to acquire the criteria weights vector. Afterwards, the adaptive particle swarm optimization(APSO) algorithm is arranged to find the optimal PV system and obtain the ranking result. Finally, the feasibility and effectiveness of this model are verified by a real-world PV systems case.

Key words:

sustainability assessment of photovoltaic (PV) system, granular computing, interval type-2 fuzzy set (IT2FSs), stochastic multi-criteria acceptability analysis (SMAA), adaptive particle swarm optimization (APSO) algorithm

摘要：

针对“碳达峰，碳中和”背景下，如何多视角评价光伏系统，助力中国光伏产业的绿色低碳发展这一现实问题，提出了可持续发展评价维度下基于数据驱动的光伏系统决策模型。首先，依据可持续评价维度，建立光伏系统综合评价指标体系。其次，针对光伏系统绩效水平存在不确定性和波动性的情况，构建区间信息粒和区间二型模糊集用以量化评价信息。再次，针对光伏系统评价指标权重偏好未知的情况，利用随机多准则可接受分析方法，通过蒙特卡罗模拟生成指标权重向量空间，获取指标权重向量；进一步，运用自适应粒子群算法得出排序结果。最后，通过实际光伏系统案例验证了该方法的可行性与有效性。

关键词:

光伏系统可持续评价, 粒计算, 区间二型模糊集, 随机多准则可接受度分析, 自适应粒子群算法