综合社区能源系统（ICES）已成为通过有效协调多种能源来提高配电系统效率的一种有前景的解决方案。然而，ICES的运行优化受到包括电力、天然气和热力在内的异构网络物理约束的阻碍。由于网络约束的非线性以及多网络协调的高度复杂性，这些挑战难以应对。因此，本文提出一种新颖的安全强化学习（SRL）算法，以优化ICES的多网络约束运行问题。首先，建立一个考虑综合需求响应（IDR）、多种能源设备和网络约束的综合ICES模型。随后，提出ICES的多网络运行优化问题，并将其重新表述为考虑违反物理网络约束的约束马尔可夫决策过程（C-MDP）。所提出的名为Primal-Dual Twin Delayed Deep Deterministic Policy Gradient（PD-TD3）的新型SRL算法通过采用拉格朗日乘子惩罚多网络约束违例来求解C-MDP，确保违例处于可容忍范围内，同时避免低奖励的过度保守策略。该算法准确估计训练过程的累积奖励和成本，从而在仅有部分信息的隐私保护环境中，在提高利润和减少约束违例之间实现公平平衡。一项将所提算法与基准RL算法进行比较的案例研究证明了其在增加总利润和缓解网络约束违例方面的计算性能。
英文摘要：
The integrated community energy system (ICES) has emerged as a promising solution for enhancing the efficiency of the distribution system by effectively coordinating multiple energy sources. However, the operational optimization of ICES is hindered by the physical constraints of heterogeneous networks including electricity, natural gas, and heat. These challenges are difficult to address due to the non-linearity of network constraints and the high complexity of multi-network coordination. This paper, therefore, proposes a novel Safe Reinforcement Learning (SRL) algorithm to optimize the multi-network constrained operation problem of ICES. Firstly, a comprehensive ICES model is established considering integrated demand response (IDR), multiple energy devices, and network constraints. The multi-network operational optimization problem of ICES is then presented and reformulated as a constrained Markov Decision Process (C-MDP) accounting for violating physical network constraints. The proposed novel SRL algorithm, named Primal-Dual Twin Delayed Deep Deterministic Policy Gradient (PD-TD3), solves the C-MDP by employing a Lagrangian multiplier to penalize the multi-network constraint violation, ensuring that violations are within a tolerated range and avoid over-conservative strategy with a low reward at the same time. The proposed algorithm accurately estimates the cumulative reward and cost of the training process, thus achieving a fair balance between improving profits and reducing constraint violations in a privacy-protected environment with only partial information. A case study comparing the proposed algorithm with benchmark RL algorithms demonstrates the computational performance in increasing total profits and alleviating the network constraint violations.
Techno Economic Modeling for Agrivoltaics: Can Agrivoltaics be more profitable than Ground mounted PV?
农业生态系统（AV）是一种双重土地使用方法，将太阳能发电与农业结合起来，以保护陆地生态系统并实现粮食-能源-水的协同作用。在这里，我们提出了一个系统的方法来模拟AV相对于独立的地面安装光伏（GMPV）的经济性能，并探讨模块设计配置如何影响双重粮食能源经济性能。一个非常简单的经济可行性的标准是量化的土地保护成本的双重粮食能源利润。我们探讨案例研究，包括高价值和低价值作物固定倾斜双面模块沿传统的北/南（N/S）面临或垂直东/西（E/W）面临。对于每个模块配置，阵列密度是不同的，以探索相对于GMPV的经济上可行的设计空间，用于一定范围的模块与土地成本比（M_L）-一个将模块技术（硬件和安装）成本与软件（土地购置、税收、管理费用等）相关联的位置特定的指标。成本为了抵消保护农田所需的通常较高的AV模块成本，E/W和N/S取向的模块都有利于高价值作物、降低的（<60%）模块密度和较高的M_L（>25）。相比之下，在较低的M_L下，更高的组件密度和相对于GMPV增加的上网电价（FIT）是期望的。M_L 20的经济趋势变化很大< 10 but tend to saturate for M_L>。对于低价值作物，约15%的额外FIT可以在标准模块密度下实现与GMPV的经济等效性。所提出的模型框架可以为AV利益相关者提供一个有价值的工具，以评估，预测和优化AV的技术经济设计
Techno-Economic Analysis of Shared Mobile Storage for Demand Charge Reduction
Elmore Family School of Electrical and Computer Engineering, Purdue University（普渡大学埃尔莫尔电气与计算机工程学院）；School of Advanced Engineering, Great Bay University（东莞理工学院高级工程学院）
Evaluating the Techno-Economic Viability of a Solar PV-Wind Turbine Hybrid System with Battery Storage for an Electric Vehicle Charging Station in Khobar, Saudi Arabia
针对沙特胡拜尔电动汽车充电站，研究利用 Homer 仿真优化光伏、风电与电池储能混合系统的配置、成本及控制策略，验证其技术经济可行性。
Deep Neural Koopman Operator-based Economic Model Predictive Control of Shipboard Carbon Capture System
Nanyang Technological University（南洋理工大学）；School of Chemistry, Chemical Engineering and Biotechnology（化学、化学工程与生物技术学院）；Nanyang Environment & Water Research Institute (NEWRI)（南洋环境与水研究所）
