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With the increasing penetration of distributed photovoltaics (PVs) in active distribution networks (ADNs), the risk of voltage violations caused by PV uncertainties is significantly exacerbated. Since the conventional voltage regulation strategy is limited by its discrete devices and delay, ADN operators allow PVs to participate in voltage optimization by controlling their power outputs and cooperating with traditional regulation devices. This paper proposes a decoupling rolling multi-period reactive power and voltage optimization strategy considering the strong time coupling between different devices. The mixed-integer voltage optimization model is first decomposed into a long-period master problem for on-load tap changer (OLTC) and multiple short-period subproblems for PV power by Benders decomposition algorithm. Then, based on the high-precision PV and load forecasts, the model predictive control (MPC) method is utilized to modify the independent subproblems into a series of subproblems that roll with the time window, achieving a smooth transition from the current state to the ideal state. The estimated voltage variation in the prediction horizon of MPC is calculated by a simplified discrete equation for OLTC tap and a linearized sensitivity matrix between power and voltage for fast computation. The feasibility of the proposed optimization strategy is demonstrated by performing simulations on a distribution test system.

To address the problem of the supply−demand imbalance caused by network transmission losses in integrated power and heating systems (IPHS), this paper presents an optimal economic dispatch strategy to minimize system operation cost and realize coordination and optimization between power and heat. Firstly, an innovative economic dispatch model considering transmission losses is developed, where both power and heat transmission losses models are established with good precision together. In addition, the coordination equation is derived from the formulated nonlinear, multi-constrained coupling optimization problem, where the coordination relationship of units’ outputs is clearly analyzed in an analytic way. Then, a double- λ -iteration algorithm is proposed, which can not only effectively solve the nonlinear coupling optimization problem but also decrease computation burden with faster convergence rate. Finally, simulations performed on five case studies illustrate the satisfying performance of the presented strategy.

The requirement for large-scale renewable energy integration promotes the upgrading of distribution system from single AC system to hybrid AC-DC system. In order to improve the penetration of renewable energy located in remote areas, this paper proposes an AC-DC configuration where the renewable energy generated by distributed generators is collected through a DC system and transmitted to the AC distribution system through several collection lines. The planning model aims to minimize the total investment and operating costs over the planning horizon. A second order conic programming is introduced to formulate the optimal power flow in AC-DC distribution system. The reliability of DC system in dealing with N−1 contingency is further improved from the aspect of topology. Numerical simulations on a test AC-DC distribution system demonstrate the validity of the proposed planning model.