基于信息间隙决策理论的储能电站鲁棒优化配置

储能电站在转移电力需求、平抑可再生能源和负荷波动等方面发挥着重要的作用,储能电站容量优化配置是其在规划建设过程中所需要解决的关键问题。文中提出一种基于信息间隙决策理论（IGDT）的双层鲁棒优化配置模型,为解决系统在运行过程中的不确定参数带来的负面影响,模型下层搭建以经济性为目标的储能电站日前鲁棒优化运行模型;模型上层引入IGDT将经济性和鲁棒性相结合,在一定的预期目标下最大化系统能够承受的波动性,使用Benders分解法求解模型,并以IEEE 33节点配电网系统验证方案有效性,结果表明所提方案可以兼顾经济性与鲁棒性,为储能规划提供参考依据。     

基于IGDT鲁棒模型的风电爬坡事件协调调度决策

随着风电渗透率的提高,大功率风电爬坡可造成系统功率不平衡,甚至导致停电事故。提出了基于信息差距决策理论(information gap decision theory,IGDT)鲁棒模型的风电爬坡事件协调调度决策方法。分析了各参与方的调度成本及约束条件,采用常规机组、风电场及需求侧协调调度的方式,降低风电爬坡事件的不利影响,并保证决策方案的经济性;利用IGDT方法处理风电功率的不确定性,构建最恶劣爬坡场景,制定针对预期成本具有鲁棒性的决策方案。算例及实际系统不同情形下调度决策方案的分析和比较,验证了所提调度决策方法的鲁棒性和经济性。     

基于IGDT的光储EV混合系统鲁棒优化调度

为减弱光伏发电出力的不确定性给光伏并网带来的影响,研究了光储电动汽车(electric vehicle,EV)混合系统优化调度。光储电站运营商以充电补贴的方式引导部分电动汽车改变其原始充电习惯,在光伏发电出力较高时段结合储能系统消纳过剩光伏,并以运营商收益最大化为目标建立光储EV混合系统日前优化调度模型;同时考虑到光伏预测值与实际值之间的偏差,利用信息差距决策理论(information gap decision theory, IGDT)模拟其偏差,得到不同预期目标下的决策解,为运营商提供决策支持,并结合算例进行分析,验证模型的合理性。     

硅直接键合(SDB)技术在新型电力电子器件应用中的新进展

综述了硅直接键合（SDB）技术在绝缘栅双极晶体管（IGBT）、静电感应晶闸管（SITH）及MOS控制可关断晶闸管（MTO）等器件中的新应用,说明了SDB技术在新型电力电子器件应用中的新方法和新思路。在此基础上,提出了用SDB技术制作集成门极换流晶闸管（IGCT）和集成门极双晶体管（IGDT）的设想,并给出了相应的工艺方案。     

含风电机组与电转气设备的虚拟电厂竞价策略

随着电转气(power-to-gas,P2G)技术的不断发展和商业化应用,风电机组(wind power generation unit,WPGU)与电转气设备的协同运行逐步受到关注。电转气设备的运行方式比较灵活,可有效平衡风电出力波动,二者可协同形成虚拟电厂(virtual power plant,VPP)参与电力市场运营。在此背景下,研究了含风电机组与电转气设备的虚拟电厂竞价策略。首先构建了含风电机组与电转气设备的虚拟电厂参与电力市场运营的模型架构。之后,以最大化总体收益为目标,构建了虚拟电厂在日前电力市场的竞价策略模型,并确定虚拟电厂自调度运行策略。然后,考虑到风电出力预测误差的不确定性,发展了基于信息间隙决策理论(information gap decision theory,IGDT)的鲁棒竞价策略和机会竞价策略,以反映虚拟电厂不同的风险偏好水平。接着,采用成熟的商用求解器AMPL/IPOPT求解所构造的非线性优化模型,确定针对预期成本的鲁棒和机会竞价策略。最后,以北欧电力市场的实际数据为例说明所构建的优化模型和采用的求解方法的基本特征。     

Risk-constrained design of autonomous hybrid refueling station for hydrogen and electric vehicles using information gap decision theory

The proposed autonomous hybrid charging station in this paper is energized by a photovoltaic (PV) system, which should provide electric vehicles (EVs), and water electrolyzer (WE) with electricity. The WE operates by using electricity to produce and store hydrogen to feed hydrogen vehicles (HVs). Moreover, a fuel cell (FC) is allocated to the system, which uses the stored hydrogen to regenerate electricity the PV system is beyond reach. A supplementary diesel generator is also installed in the charging station to avoid power shortage as a conservative measurement. The hydrogen and electric demand of the station is accompanied by uncertainties, which should be taken into account in designing the charging station. Therefore, information-gap decision theory (IGDT) is employed to deal with the uncertainties. This approach provides the investor with three different strategies of risk-averse strategy (RAS), risk-neutral strategy (RNS), and risk-seeker strategy (RSS), which can help the investor with making a better decision. The outcome of the simulation proved that in RAS if the investor decides to invest 13.9% more capital, based on the robustness function, the charging station withstands the 9.6% deviation of uncertain parameters’ fraction error. However, should the investor decide to take risks in the construction of the charging station, by paying 13.9% less, the system is 10.7% fragile to the information-gap of uncertainties. Besides, the rated power of the PV system reaches from 1612 kW in RNS to 1731 kW in RAS while it decreased to 1479 kW in RSS.     

A hybrid approach based on IGDT–MPSO method for optimal bidding strategy of price-taker generation station in day-ahead electricity market

This paper considers a price-taker generation station producer that participates in a day-ahead market. The producer behaves as a price-taker participant in the day-ahead electricity market. In electricity market, the price-taker producer could develop bidding strategies to maximize own profits. While making optimal bidding strategy, the market price uncertainty needs to be considered as they have direct impact on the expected profit and bidding curves. In this paper, a hybrid approach based on information gap decision theory (IGDT) and modified particle swarm optimization (MPSO) is used to develop the optimal bidding strategy. Information gap decision theory is used to model the optimal bidding strategy problem. It assesses the robustness/opportunity of optimal bidding strategy in the face of the market price uncertainty while price-taker producer considers whether a decision risk-averse or risk-taking. The optimization problems to delivering IGDT approach are solved using MPSO. It is shown that risk-averse or risk-taking decisions might affect the expected profit and bidding curve to day-ahead electricity market. The IGDT–MPSO method is illustrated through a case study and compared to IGDT–MINLP method.     

基于改进信息间隙决策理论的考虑决策者风险态度的社区虚拟电厂经济-能源-环境调度策略选择

针对决策者风险态度(decision-makers'risk attitudes,DMRA)和不确定性对社区综合能源系统调度策略的影响,提出了社区虚拟电厂(community virtual power plant,CVPP)的多目标调度模型.首先,建立了考虑DMRA的CVPP模型和经济-能源-环境多目标满意度模型.其次,考虑可再生能源、负荷和DMRA的不确定性,对信息间隙决策理论(information gap decision theory,IGDT)模型进行了改进.第三,在考虑DMRA的基础上,拓展自信双层语言术语下的改进VIKOR方法.最后,以某居民区为例,对该模型的有效性进行了验证.结果表明:1)基于DMRA的CVPP提供了切合实际的调度策略.2)实施需求响应后,居民成本和净碳排放分别降低了9％和91％,提高了能源供应商的利润和可再生能源的利用率,所构建的IGDT模型也改进了多个目标.3)改进后的IGDT模型的不确定性和偏差因素允许采用多种调度策略.同时,改进的VIKOR方法为决策者选择策略提供了一种新的方法.该模型为调度策略的选择提供了指导,同时也为鼓励可再生能源的使用提供了途径.     

基于信息间隙决策理论的含DG和EV的主动配电网优化运行

在电动汽车和清洁能源发电大量接入配电网的背景下,电动汽车无序充电与清洁能源出力的不确定性给配电网运行带来负面影响。首先,通过蒙特卡洛模拟生成电动汽车出行特性,以分时电价为调节手段制定有序充电控制策略,并考虑分布式发电出力的不确定性,建立基于信息间隙决策理论（information gap decision theory, IGDT）的主动配电网优化运行模型;之后,以电网侧、用户充电、环境侧总运行成本最小为目标,包含多种主动管理设备约束,并考虑决策者不同的风险偏好,分别建立机会模型与鲁棒模型,为制定调度方案提供决策依据;最后,通过改进的PG&E 69节点算例验证了IGDT模型有效性。