Dispatchable Region for Active Distribution Networks Using Approximate Second-order Cone Relaxation

Uncertainty in distributed renewable generation threatens the security of power distribution systems.The concept of dispatchable region is developed to assess the ability of power systems to accommodate renewable generation at a given operating point.Although DC and linearized AC power flow equations are typically used to model dispatchable regions for transmission systems,these equations are rarely suitable for distribution networks.To achieve a suitable trade-off between accuracy and efficiency,this paper proposes a dispatchable region formulation for distribution networks using tight convex relaxation.Secondorder cone relaxation is adopted to reformulate AC power flow equations,which are then approximated by a polyhedron to improve tractability.Further,an efficient adaptive constraint generation algorithm is employed to construct the proposed dispatchable region.Case studies on distribution systems of various scales validate the computational efficiency and accuracy of the proposed method.

基于最优潮流的含多微网的主动配电网双层优化调度

多个微电网并入主动配电网(Active Distribution Network,ADN)会对ADN系统的经济性和可靠性产生影响。采用传统的多微网与ADN全网统一优化调度方法时,若微网中风/光出力一旦出现波动,难以高效精确地求出系统的最优潮流,甚至造成无解。因此提出一种基于双层规划的多微网并网优化调度模型。上层模型中各微网作为电源并入ADN,以ADN系统潮流平衡为约束,建立最优潮流模型。运用二阶锥松弛技术将非凸非线性的潮流模型转化为凸可行域的二阶锥规划模型,并调用Gurobi求解器求解。下层模型以上层优化出的联络线功率为约束,建立并网微电网内可控电源的调度模型,并采用结合Tent映射混沌和NDX交叉技术的改进遗传算法(GA)求解。以并入多微网的调整IEEE33节点系统为算例,仿真算例表明双层规划的调度模型及算法具有可行性且在此模型下含多微网的ADN系统有更好的经济性。同时当风/光发电出现波动时,下层模型仍然可以进行局部调整优化,从而降低了微电网波动对ADN系统的影响,提高了系统的可靠性和鲁棒性。

“碳中和”目标下电气互联系统有功-无功协同优化模型

随着碳中和目标的提出,新能源机组逐步成为主力电源,其波动性给系统特别是配电网的控制和优化带来了巨大的挑战.为了解决高比例可再生能源接入配网带来的无功问题,提出一种计及无功优化和多能协同交互的源-网-荷-储集中优化调度模型.该模型以多能系统运行成本最优、网络损耗最小、碳排放量最小为目标,考虑无功补偿、储能调节以及能量转换等手段,以实现电气互联系统的安全低碳经济调度.采用改进二阶锥松弛法对配网模型中非线性等式约束进行凸松弛,采用大M法对离散无功补偿装置的投切容量进行线性化表达,将模型转换为混合整数二阶锥规划问题进行求解.仿真结果表明,所提方法能够有效补偿风机并网点所需无功,协调电、气之间的能量交互,从而提高大规模可再生能源接入后配网的稳定性与弹性,促进可再生能源消纳.

Convexification of Hybrid AC-DC Optimal Power Flow with Line-Commutated Converters

Line-commutated converter (LCC)-based high-voltage DC (HVDC) systems have been integrated with bulk AC power grids for interregional transmission of renewable power. The nonlinear LCC model brings additional nonconvexity to optimal power flow (OPF) of hybrid AC-DC power grids. A convexification method for the LCC station model could address such nonconvexity but has rarely been discussed. We devise an equivalent reformulation for classical LCC station models that facilitates second-order cone convex relaxation for the OPF of LCC-based AC-DC power grids. We also propose sufficient conditions for exactness of convex relaxation with its proof. Equivalence of the proposed LCC station models and properties, exactness, and effectiveness of convex relaxation are verified using four numerical simulations. Simulation results demonstrate a globally optimal solution of the original OPF can be efficiently obtained from relaxed model.

Application of Modified Progressive Hedging for Stochastic Unit Commitment in Electricity-Gas Coupled Systems

The increasing number of gas-fired units has significantly intensified the coupling between electric and gas power networks.Traditionally,nonlinearity and nonconvexity in gas flow equations,together with renewable-induced stochasticity,resulted in a computationally expensive model for unit commitment in electricity-gas coupled integrated energy systems(IES).To accelerate stochastic day-ahead scheduling,we applied and modified Progressive Hedging(PH),a heuristic approach that can be computed in parallel to yield scenario-independent unit commitment.Through early termination and enumeration techniques,the modified PH algorithm saves considerable com,putational time for certain generation cost settings or when the scale of the IES is large.Moreover,an adapted second-order cone relaxation(SOCR)is utilized to tackle the nonconvex gas flow equation.Case studies were performed on the IEEE 24.bus system/Belgium 20-node gas system and the IEEE 118-bus system/Belgium 20-node gas system.The computational efficiency when employing PH is 188 times that of commercial software,and the algorithm even outperforms Benders Decomposition.At the same time,the gap between the PH algorithm and the benchmark is less than 0.01% in both IES systems,which proves that the solutions produced by PH reach acceptable optimality in this stochastic UC problem.