绿色能源项目风险管理方法与决策支持系统综述

A Review of Risk Management Methods and Decision Support Systems for Green Energy Projects

摘要: 面向绿色能源项目全生命周期的风险识别、评估与预警需求，本文围绕风险管理方法与决策支持系统(DSS)构建开展综述研究。首先从风险来源与传导机理出发，构建覆盖技术、市场、政策与监管以及环境与合规等维度的风险分类框架，并在生命周期视角下明确建设期、并网期与运营期的风险侧重点与数据口径。针对多业态场景，本文在章节内部区分风电、光伏与储能三类对象的风险触发特征与建模差异：风电侧更突出资源不确定引起的短时波动与并网约束联动，光伏侧更突出辐照驱动的分段特征与日内不确定性，储能侧更突出状态依赖、退化累积与安全边界约束。随后，本文对多准则综合评价、客观赋权排序、模糊评价与概率推断等主流风险评估方法进行归纳，对比其在数据条件、应用阶段与可解释性要求下的适用场景，并梳理近年数据驱动模型在风险预测中的评价口径与验证指标体系。最后，结合工程落地需求，本文总结DSS的总体架构与关键实现要点，强调在数据接入、指标口径治理、模型库管理与实时监控预警闭环之间建立可追溯链路，为多业态绿色能源项目风险管理提供可复用的技术路线与实现参考。

Abstract: To support lifecycle risk identification, assessment, and early warning in green energy projects, this paper presents a structured review of risk management methods and decision support systems (DSS). We first develop a risk taxonomy covering technical, market, policy-and-regulatory, and environmental-and-compliance dimensions, and clarify stage-specific emphases and data definitions across construction, grid-integration, and operation phases. For multi-technology settings, we explicitly differentiate wind, photovoltaic (PV), and energy storage in terms of risk triggers and modeling requirements: wind projects are characterized by short-term variability and its coupling with grid constraints; PV projects are driven by irradiance-induced regime changes and intra-day uncertainty; energy storage exhibits strong state dependence, cumulative degradation, and safety-boundary constraints. We then summarize and compare mainstream assessment approaches—multi-criteria decision making, objective weighting and ranking, fuzzy evaluation, and probabilistic inference—by linking method selection to data availability, application phase, and interpretability needs, and we consolidate recent practices in data-driven risk prediction with consistent evaluation metrics. Finally, we outline a DSS reference architecture and key implementation considerations, highlighting traceable pipelines across data ingestion, metric governance, model management, and real-time monitoring and warning loops. The review provides reusable guidance for deploying risk-aware DSS in multi-technology green energy projects.