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Distributionally Robust Unit Commitment in Coordinated Electricity and District Heating Networks
IEEE Transactions on Power Systems ( IF 6.5 ) Pub Date : 2020-05-01 , DOI: 10.1109/tpwrs.2019.2950987
Yizhou Zhou , Mohammad Shahidehpour , Zhinong Wei , Zhiyi Li , Guoqiang Sun , Sheng Chen

Coordinated operations of electricity and district heating networks offer a potential for mitigating inherent variability of renewable energy sources (RES) in the ongoing transition to smart grids. This paper proposes a two-stage distributionally robust optimization (DRO) approach to determine the optimal day-ahead unit commitment in coordinated electricity and district heating networks with variable RES power output. The proposed formulation is to minimize the worst-case expected total cost over an ambiguity set comprising a family of probability distributions with given support and moments of RES power output. As such, the proposed DRO approach can overcome the limitations of stochastic programming in its inherent dependence of exact probability distributions along with a huge computational burden, but also becomes less conservative than classical robust optimization. The pertinent DRO model is eventually reformulated as a tractable mixed-integer second-order cone (SOC) programming after employing linear decision rules and the SOC duality. Simplified affine policies are utilized to further improve computational tractability and performance. Finally, case studies are conducted based on Barry Island electricity and district heating networks. The numerical results demonstrate the decision-making superiority of the proposed method as compared with deterministic, stochastic programming, and robust optimization approaches. They also validate the computational improvement of the proposed approach by employing simplified affine policies.

中文翻译:

协调电力和区域供热网络中分布稳健的机组承诺

电力和区域供热网络的协调运行为减轻可再生能源 (RES) 在向智能电网的持续过渡过程中的固有可变性提供了潜力。本文提出了一种两阶段分布稳健优化 (DRO) 方法,以确定具有可变 RES 功率输出的协调电力和区域供热网络中的最佳日前机组承诺。建议的公式是在包含一系列概率分布的模糊集上最小化最坏情况的预期总成本,这些概率分布具有给定的支持和 RES 功率输出矩。因此,所提出的 DRO 方法可以克服随机编程的局限性,即其对精确概率分布的固有依赖性以及巨大的计算负担,但也变得比经典的鲁棒优化更不保守。在采用线性决策规则和 SOC 对偶性之后,相关的 DRO 模型最终被重新表述为易于处理的混合整数二阶锥 (SOC) 规划。简化的仿射策略被用来进一步提高计算的易处理性和性能。最后,案例研究基于巴里岛电力和区域供热网络进行。数值结果表明,与确定性、随机规划和鲁棒优化方法相比,所提出方法的决策优越性。他们还通过采用简化的仿射策略验证了所提出方法的计算改进。在采用线性决策规则和 SOC 对偶性之后,相关的 DRO 模型最终被重新表述为易于处理的混合整数二阶锥 (SOC) 规划。简化的仿射策略被用来进一步提高计算的易处理性和性能。最后,案例研究基于巴里岛电力和区域供热网络进行。数值结果表明,与确定性、随机规划和鲁棒优化方法相比,所提出方法的决策优越性。他们还通过采用简化的仿射策略验证了所提出方法的计算改进。在采用线性决策规则和 SOC 对偶性之后,相关的 DRO 模型最终被重新表述为易于处理的混合整数二阶锥 (SOC) 规划。简化的仿射策略被用来进一步提高计算的易处理性和性能。最后,案例研究基于巴里岛电力和区域供热网络进行。数值结果表明,与确定性、随机规划和鲁棒优化方法相比,所提出方法的决策优越性。他们还通过采用简化的仿射策略验证了所提出方法的计算改进。简化的仿射策略被用来进一步提高计算的易处理性和性能。最后,案例研究基于巴里岛电力和区域供热网络进行。数值结果表明,与确定性、随机规划和鲁棒优化方法相比,所提出方法的决策优越性。他们还通过采用简化的仿射策略验证了所提出方法的计算改进。简化的仿射策略被用来进一步提高计算的易处理性和性能。最后,案例研究基于巴里岛电力和区域供热网络进行。数值结果表明,与确定性、随机规划和鲁棒优化方法相比,所提出方法的决策优越性。他们还通过采用简化的仿射策略验证了所提出方法的计算改进。
更新日期:2020-05-01
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