随着大量分布式光伏（distributed photovoltaic，DPV）并网，目前配电网中出现了日间过压和夜间低压并存的电压越限问题，并进一步影响了网络的经济运行。本工作基于此提出了两阶段光储系统协同运行的优化调度策略。该策略第一阶段通过计算节点的电压灵敏度来确定待调节的储能节点与充放功率，以及光伏可调节点；第二阶段建立了光储运行优化模型。该模型以储能的调度成本、购售电成本以及网损成本之和最小为目标，以网络潮流、节点电压、储能SOC（state of charge）、光伏的无功可调容量等作为约束，通过粒子群算法对该模型进行求解，可以得到光储系统日调度出力策略。最后，以某地区31节点的实际配电网作为算例，验证了本工作方法的有效性。算例结果表明，该策略可以通过光储的协同调度，有效治理电网中的电压越限问题，并且在保障配电网电压安全的同时，实现优化运行成本的目标。

Although distributed photovoltaics (DPVs) are increasingly being integrated into power grids, the problems of daytime overvoltage and nighttime low voltage in the distribution the network persist, which affect network economy. To overcome this problem, a two-stage optimization operation strategy involving DPVs and energy storage systems (ESSs) was proposed. In the first stage of the strategy, the position and amount of charge and discharge of the ESSs were determined by calculating the voltage sensitivity of the node and the adjustable photovoltaic reactive power node. Next, the optimization operation model was established. The model minimizes the sum of dispatching cost of ESSs, power exchange cost, and network loss cost. The constraints of the study include the distribution network, node voltage, state of charge (SOC) of ESSs, and adjustable reactive power capacity of photovoltaic inverters. In this model, particle swarm optimization was used to devise the daily scheduling output strategy of DPVs and ESSs. Finally, the effectiveness of the proposed method was verified using a 31-node actual distribution network. The simulation results revealed that the proposed strategy can effectively solve the voltage violation problem through the cooperative dispatching of DPVs and ESSs. Thus, the operating cost of the distribution network can be optimized while ensuring voltage safety.

随着电力体制改革和售电侧市场的开放,端对端交易逐渐成为促进分布式能源消纳的重要手段。然而,分布式能源存在容量小、位置分散等问题,亟需一种能源管理模式对其聚合管理,并且现有交易机制中,存在过网费设计无法有效促进能源就近消纳的问题。在此背景下,文章提出了配电网下多虚拟电厂间计及过网费的端对端电能交易方法研究。首先,设计了虚拟电厂间端对端电能交易的总体架构;其次,建立虚拟电厂优化模型,对虚拟电厂的所属节点进行出力优化,并根据优化所得的偏差电量制定下一匹配阶段的报量策略和报价策略;随后,设计计及过网费的交易匹配机制,并建立基于配电网交流潮流模型的安全校验模型和阻塞管理模型,在保证配电网安全稳定运行的前提下对初始匹配订单进行管理与削减。算例表明,文章所提的多虚拟电厂间端对端电能交易方法可有效促进能源就近交易并降低虚拟电厂的综合运行成本。

With the advances in power systems and the opening of electricity retail markets, peer-to-peer transactions have become essential for promoting the integration of distributed energy resources. However, owing to their small individual capacity and dispersed locations, such resources demand an efficient energy management paradigm for aggregation. Furthermore, the existing transaction mechanisms exhibit shortcomings in the design of network tariff fees, hindering their effectiveness in promoting the localized consumption of energy. In this context, this paper proposes a mechanism and model for peer-to-peer energy trading between virtual power plants (VPPs) considering the power flow of the distribution network under multiple access nodes. First, the overall architecture of peer-to-peer power trading between multiple VPPs is designed. Second, an optimization model for VPPs is established to optimize the output of VPPs at different connection points. Based on the optimization results, strategies of bidding for the next matching phase are formulated. Subsequently, a trading matching mechanism that considers network charges is designed, and safety verification models and congestion management models based on the optimal power flow of the distribution network are established to manage the matched orders. Finally, the effectiveness of the proposed mechanisms and models is validated through a case study in the IEEE33 node system.

随着“双碳”目标的提出，配电台区接入大量分布式光伏、电动汽车、微网以及智能建筑等新元素，在给中低压配网安全运行带来挑战的同时也蕴含着巨大的灵活调节潜力。本工作以配电台区的智能建筑为例，提出一种含虚拟储能系统的建筑微网多时间尺度优化调度方法。首先对建筑围护结构的动态特性进行建模，构建了对建筑灵活性进行定量描述的虚拟储能模型，通过对虚拟储能的充放电过程进行优化调度来实现对建筑灵活性的定量利用；随后，在配电台区灵活资源多时间尺度优化调度模型中详细考虑了建筑的可调虚拟储能特性，分别在日前调度和日间修正两个时间尺度对建筑虚拟储能进行充分利用，提出以运行成本最低为目标的日前经济调度方法和跟踪微网联络线设定值的日间修正方法。最后以夏季制冷场景为例，利用典型建筑微网系统验证了本文所提多时间尺度优化调度方法的有效性。结果表明，本文所提方法可有效调度建筑的虚拟储能能力，实现在日前运行阶段降低建筑微网的运行成本，在日间运行阶段有效平抑可再生能源的随机波动。

With the proposal of the "dual carbon" goal, the distribution station area is connected to many new elements, such as distributed photovoltaics, electric vehicles, microgrids, and smart buildings, posing challenges to the safe operation of medium- and low-voltage distribution networks and offering considerably flexibility potential. Taking smart buildings connected to the distribution station area as an example, this study proposes a multi-time scale optimal scheduling method for a building microgrid considering a virtual storage system. Firstly, the thermal dynamics of the envelope of the building is modeled, and a virtual energy storage model that quantitatively describes the flexibility of buildings are constructed. The flexibility of buildings can be used by optimizing the charging and discharging powers of the virtual energy storage. Then, the adjustable virtual energy storage characteristics of buildings are considered in detail in the multi-time scale optimal scheduling model of flexible resources in distribution stations. The proposed multi-time scale optimal scheduling model can leverage the virtual energy storages at day-ahead and the intra-day stages. A day-ahead economic scheduling method aiming at the lowest operating cost and an intra-day correction method for tracking the setting value of the microgrid contact line is proposed. Finally, taking the cooling scenario in summer as an example, the effectiveness of the proposed multi-time-scale optimal scheduling method is verified using a typical building microgrid system. The results show that this method can effectively schedule the virtual energy storage capacity of the building, reduce the operating cost of the building microgrid in the day-ahead operation stage, and effectively mitigate the random fluctuation of renewable energy in the intra-day stage.