考虑风光不确定性与灵活性指标的园区综合能源系统最优调度

针对风光高渗透率的园区综合能源系统调度过程中灵活性不足的问题,提出一种考虑风光不确定性与功率型灵活性指标的园区级综合能源系统日前调度策略。在调度模型中通过机会约束理论对风光的不确定性提供备用服务,利用功率型灵活指标对灵活性供给进行量化评价,在此基础上构建兼顾经济成本与灵活性供给的日前优化调度模型,采用混合整数规划的方法求解;仿真结果表明,该调度策略有效保证了系统运行的灵活性与经济性。

用于提升风电消纳能力的高载能负荷调度策略研究

近年来,我国大力发展以风电为主的新能源发电,尤其是在我国风资源丰富的“三北”地区,但这些地区往往负荷需求量较小,与风电装机容量难以匹配,因此在我国“三北”地区存在大量弃风现象。为解决这一问题,文中介绍了用于提升风电消纳能力的高载能负荷调度策略。参与调度的负荷为两种高载能负荷,分别为电解铝负荷和铁合金负荷。根据两种高载能负荷的运行特性及调节特性,对其进行分类并建立数学模型,并根据两类负荷的不同特性,分别建立日前调度和日内调度的数学模型,通过算例验证了所提调度策略的有效性。

Optimal Scheduling of Regional Integrated Energy Systems Considering Hybrid Demand Response

Flexible load can optimize the load curve,which is an important means to promote renewable energy consumption.The peculiarities of electricity,heat,cooling and gas loads are analyzed in this paper,considering the fuzzy degree of human perception for water temperature,and the characteristic model of hot water load is established.Considering the fuzzy degree of human perception of ambient temperature,the characteristic model of cooling load is established by using PMV and PPD index.Meanwhile,considering four combinations of cut load,translatable load,transferable load and alternative load,and considering the coupling relationship of composite parts,different response models of load are established respectively.With the minimum cost of the system,including operation and compensation costs as the objective function,the optimization scheduling model of the regional integrated energy system is established,and the Gurobi solver is used for simulation analysis to solve the optimal output and load response curve of each piece of equipment.The results show that the load curve can be optimized,the flexible regulation ability of the regional integrated energy system can be enhanced,the energy loss of the system can be reduced,and the wind power consumption ability of the system can be increased by considering the integrated demand response.

发电商长期电能分配策略研究

电力市场改革使得发电商能够自主的进行电力交易,同样也使得电力交易的方式多样化。发电商既可以在双边合同市场中进行电力交易,也可以在日前市场的实时电能市场和辅助服务市场进行电力交易。因此发电商应解决如何将有限的电力合理的分配到各市场中,从而使得自身效用最大的问题。该文基于双边合同市场和日前市场的市场交易结构,建立了发电商长期电力交易的效用模型,并考虑了机组的运行约束。该模型的优化解能得到不同风险偏好的发电商面对同一电力双边合同的优化容量分配策略以及同一发电商对于不同价格的电力合同的优化分配策略。该文的研究为发电商长期的分配策略提供了决策依据。

日前电力市场环境下的实时调度

全面阐述了在日前电力市场环境下如何将日前发电计划转化为实时调度指令,以确保日前计划具备调度和结算上的双重意义。结合超短期负荷预测的结果,充分考虑日前市场的实施现状,提出了基于日前计划的首选分配策略和基于可调裕度、发电进度完成率、合同电价的辅助分配策略。对稳定断面的潮流控制、紧急控制提高控制性能标准(CPS)指标、翻牌机制防止破坏CPS指标以及增强系统抗干扰能力等关键问题进行了详细阐述。根据该设计思想建设的超短期实时调度系统已在浙江电网投入使用,取得了良好效果,有效保证了市场出清的严肃性。

考虑源荷协调的含储热光热电站和风电系统的日前-日内调度策略

为提高电力系统风电消纳能力,结合源荷两侧的可调节资源,提出含储热光热电站和风电系统的日前-日内调度策略。首先,根据含储热光热电站能量时移特性以及居民用电负荷响应时间长的特点,结合价格型需求响应,以系统运行成本以及日前弃风量最小为目标,构建日前调度模型;其次,根据考虑含储热光热电站的灵活调节能力以及高载能负荷响应时间短的特点,结合日前调度计划与日内风电预测,以系统日内调节成本与日内弃风量最小为目标,构建日内调度模型。算例结果表明:本文所提调度策略能够充分利用现有源荷两侧可调节资源及其特点,在促进系统风电消纳的同时,降低了系统成本。

HVAC energy cost minimization in smart grids: A cloud-based demand side management approach with game theory optimization and deep learning

In this paper, we present a novel cloud-based demand side management (DSM) optimization approach for the cost reduction of energy usage in heating, ventilation and air conditioning (HVAC) systems in residential homes at the district level. The proposed approach achieves optimization through scheduling of HVAC energy usage within permissible bounds set by house users. House smart home energy management (SHEM) devices are connected to the utility/aggregator via a dedicated communication network that is used to enable DSM. Each house SHEM can predict its own HVAC energy usage for the next 24 h using minimalistic deep learning (DL) prediction models. These predictions are communicated to the aggregator, which will then do day ahead optimizations using the proposed game theory (GT) algorithm. The GT model captures the interaction between aggregator and customers and identifies a solution to the GT problem that translates into HVAC energy peak shifting and peak reduction achieved by rescheduling HVAC energy usage. The found solution is communicated by the aggregator to houses SHEM devices in the form of offers via DSM signals. If customers’ SHEM devices accept the offer, then energy cost reduction will be achieved. To validate the proposed algorithm, we conduct extensive simulations with a custom simulation tool based on GridLab-D tool, which is integrated with DL prediction models and optimization libraries. Results show that HVAC energy cost can be reduced by up to 36% while indirectly also reducing the peak-to-average (PAR) and the aggregated net load by up to 9.97%.

基于改进沙猫群算法的微电网日前优化调度

微电网系统包括多种分布式能源,为降低微电网综合发电成本,提高供电的可靠性和安全性,合理安排微电网日前调度计划,以运行成本和环境保护成本最低为目标函数,首先,建立由水、光、微型燃气轮机、储能装置组成的微电网日前优化调度模型;然后,将反向学习策略和柯西变异算子融入到沙猫群算法中,提出一种改进沙猫群算法求解上述模型;最后,通过与其他算法进行调度的结果对比,结果表明改进沙猫群算法能更合理地安排各机组的出力计划,明显降低微电网运行综合成本,同时减少二氧化碳排放量,实现了微电网的经济环保优化调度。

计及电动汽车接入的区域综合能源系统双层日前协调优化调度

规模化电动汽车(electric vehicles,EV)的接入对区域综合能源系统(regional integrated energy system,RIES)优化调度提出了新的挑战,文章提出了计及分布式电源和电动汽车源荷双向互动的RIES双层日前优化调度策略。首先,采用蒙特卡洛法模拟电动汽车无序充电,在此基础上利用分时电价引导负荷侧的电动汽车有序充电;然后,基于电力市场和碳交易市场,充分考虑源侧风光出力和负荷侧价格型需求响应等过程中的不确定性因素,以日系统经济成本和碳交易成本最低为目标进行系统优化调度,并采用改进粒子群算法进行求解;最后,通过仿真算例分析可得,文章提出的双层优化调度策略能够有效降低系统的负荷峰谷差,在实现电动汽车有序充电的同时,能够有效提升用户用电满意度、降低碳排放量以及增加系统经济效益。