We employ uncertain programming to investigate the competitive logistics distribution center location problem in uncertain environment, in which the demands of customers and the setup costs of new distribution centers...We employ uncertain programming to investigate the competitive logistics distribution center location problem in uncertain environment, in which the demands of customers and the setup costs of new distribution centers are uncertain variables. This research was studied with the assumption that customers patronize the nearest distribution center to satisfy their full demands. Within the framework of uncertainty theory, we construct the expected value model to maximize the expected profit of the new distribution center. In order to seek for the optimal solution, this model can be transformed into its deterministic form by taking advantage of the operational law of uncertain variables. Then we can use mathematical software to obtain the optimal location. In addition, a numerical example is presented to illustrate the effectiveness of the presented model.展开更多
With the promotion of“dual carbon”strategy,data center(DC)access to high-penetration renewable energy sources(RESs)has become a trend in the industry.However,the uncertainty of RES poses challenges to the safe and s...With the promotion of“dual carbon”strategy,data center(DC)access to high-penetration renewable energy sources(RESs)has become a trend in the industry.However,the uncertainty of RES poses challenges to the safe and stable operation of DCs and power grids.In this paper,a multi-timescale optimal scheduling model is established for interconnected data centers(IDCs)based on model predictive control(MPC),including day-ahead optimization,intraday rolling optimization,and intraday real-time correction.The day-ahead optimization stage aims at the lowest operating cost,the rolling optimization stage aims at the lowest intraday economic cost,and the real-time correction aims at the lowest power fluctuation,eliminating the impact of prediction errors through coordinated multi-timescale optimization.The simulation results show that the economic loss is reduced by 19.6%,and the power fluctuation is decreased by 15.23%.展开更多
融合高比例可再生能源的分布式能源系统中,由可再生能源的波动性和负荷的随机性所引起的不确定性,限制了供需匹配性的提升。而通过充分利用信息,可以降低系统不确定性。该文提出基于熵的能量与信息的一致标度,利用熵理论同时对由能级降...融合高比例可再生能源的分布式能源系统中,由可再生能源的波动性和负荷的随机性所引起的不确定性,限制了供需匹配性的提升。而通过充分利用信息,可以降低系统不确定性。该文提出基于熵的能量与信息的一致标度,利用熵理论同时对由能级降低造成的能量不可用性和由不确定性造成的能量不可用性进行量化。以数据中心分布式能源系统为案例,详述基于能量与信息耦合的系统配置优化方法。在考虑气象参数和数据中心计算负荷不确定性的情况下,相比于传统以初投资最低为优化目标的系统配置方案,该文提出的优化配置方案的未达到设计全年本址发电满足负荷占比(on-site energy fraction,OEF)的概率从54%降低到15%,说明该方法能够有效的降低分布式能源系统不确定性,提升供需匹配性。展开更多
在国家“数字经济”与“新基建”战略的双重推动下,近年来互联网数据中心(internet data center, IDC)快速发展并逐渐成为电力系统中重要新增负荷和需求响应(demand response, DR)资源。为支撑IDC与电力系统协调发展,提出了一种考虑灵...在国家“数字经济”与“新基建”战略的双重推动下,近年来互联网数据中心(internet data center, IDC)快速发展并逐渐成为电力系统中重要新增负荷和需求响应(demand response, DR)资源。为支撑IDC与电力系统协调发展,提出了一种考虑灵活性潜力的IDC与配电网双层协同规划框架。首先,根据IDC数据负荷及用电负荷特性建立IDC能耗模型,并从数据负荷的时空可迁移、可削减等角度分析了IDC灵活调节潜力。在此基础上,通过深入分析IDC与配电网之间的信息传递和互动关系,并考虑不确定性因素的影响,提出了以配电网运营商为上层领导者制定实时节点电价,数据中心运营商为下层跟随者基于实时电价参与DR的IDC优化选址和配置模型。该模型在充分考虑二者规划运行及价格的约束下,实现了上层净盈利最大化、下层成本最小化的双层协同规划。最后通过算例仿真和对比分析,证明了所提方法的有效性。展开更多
文摘We employ uncertain programming to investigate the competitive logistics distribution center location problem in uncertain environment, in which the demands of customers and the setup costs of new distribution centers are uncertain variables. This research was studied with the assumption that customers patronize the nearest distribution center to satisfy their full demands. Within the framework of uncertainty theory, we construct the expected value model to maximize the expected profit of the new distribution center. In order to seek for the optimal solution, this model can be transformed into its deterministic form by taking advantage of the operational law of uncertain variables. Then we can use mathematical software to obtain the optimal location. In addition, a numerical example is presented to illustrate the effectiveness of the presented model.
文摘With the promotion of“dual carbon”strategy,data center(DC)access to high-penetration renewable energy sources(RESs)has become a trend in the industry.However,the uncertainty of RES poses challenges to the safe and stable operation of DCs and power grids.In this paper,a multi-timescale optimal scheduling model is established for interconnected data centers(IDCs)based on model predictive control(MPC),including day-ahead optimization,intraday rolling optimization,and intraday real-time correction.The day-ahead optimization stage aims at the lowest operating cost,the rolling optimization stage aims at the lowest intraday economic cost,and the real-time correction aims at the lowest power fluctuation,eliminating the impact of prediction errors through coordinated multi-timescale optimization.The simulation results show that the economic loss is reduced by 19.6%,and the power fluctuation is decreased by 15.23%.
文摘融合高比例可再生能源的分布式能源系统中,由可再生能源的波动性和负荷的随机性所引起的不确定性,限制了供需匹配性的提升。而通过充分利用信息,可以降低系统不确定性。该文提出基于熵的能量与信息的一致标度,利用熵理论同时对由能级降低造成的能量不可用性和由不确定性造成的能量不可用性进行量化。以数据中心分布式能源系统为案例,详述基于能量与信息耦合的系统配置优化方法。在考虑气象参数和数据中心计算负荷不确定性的情况下,相比于传统以初投资最低为优化目标的系统配置方案,该文提出的优化配置方案的未达到设计全年本址发电满足负荷占比(on-site energy fraction,OEF)的概率从54%降低到15%,说明该方法能够有效的降低分布式能源系统不确定性,提升供需匹配性。
文摘在国家“数字经济”与“新基建”战略的双重推动下,近年来互联网数据中心(internet data center, IDC)快速发展并逐渐成为电力系统中重要新增负荷和需求响应(demand response, DR)资源。为支撑IDC与电力系统协调发展,提出了一种考虑灵活性潜力的IDC与配电网双层协同规划框架。首先,根据IDC数据负荷及用电负荷特性建立IDC能耗模型,并从数据负荷的时空可迁移、可削减等角度分析了IDC灵活调节潜力。在此基础上,通过深入分析IDC与配电网之间的信息传递和互动关系,并考虑不确定性因素的影响,提出了以配电网运营商为上层领导者制定实时节点电价,数据中心运营商为下层跟随者基于实时电价参与DR的IDC优化选址和配置模型。该模型在充分考虑二者规划运行及价格的约束下,实现了上层净盈利最大化、下层成本最小化的双层协同规划。最后通过算例仿真和对比分析,证明了所提方法的有效性。
