An increasing amount of distributed energy resources (DERs) is being integrated into both distribution systems (DS) and networked microgrids (MGs). The coordination among DS and MGs becomes essential for DS operators (DSOs) and MG operators (MGOs) considering the high uncertainties of renewable energies and load demands. In this paper, we propose a decentralized energy management framework to coordinate the power exchange between DS and MGs based on the alternating direction method of multipliers (ADMM) algorithm in a fully decentralized fashion. The energy management model in each entity (DS or MGs) is formulated using two-stage robust optimization to address the uncertainties of renewables and load demands. The problem is treated as a second order cone programming (SOCP) one based on a relaxed distflow model. Moreover, the robust model is solved by column and constraint generation (CCG) algorithm, where cutting planes are introduced to ensure the exactness of second order cone relaxation (SOCR). Numerical results on a modified IEEE 33-bus system with three MGs validate the effectiveness of the proposed method.

Day-ahead scheduling has played a significant role in the operation of distribution systems, aiming at minimizing the total operation cost. With the higher integration of information and communication technologies (ICTs) and intelligent electronic devices, distribution system operators (DSOs) may seek to optimally control the active management elements to increase the economic and technical performance of future smart distribution systems. This paper focuses on an integrated day-ahead scheduling scheme which is formulated as a mixed integer second order cone programming (MISOCP) problem to obtain the optimum solution by means of the relaxed power flow model (RPFM). The detailed technical constraints for all the elements are accommodated based on the RPFM-based optimization method. Meanwhile, active management cost and punishment cost of wind power curtailed and customer energy not supplied are also considered in the design objectives of this study. In order to address the uncertainties, a stochastic robust optimization method is first introduced to decide the active management elements (including capacitor bank, electrical storage system, controllable load and switch) as the robust decision scheme. Afterwards, the column-and-constraint generation algorithm (CCG) is applied to solve the proposed stochastic robust two-stage optimization model. Numerical results based on the 33-bus system are obtained to verify the effectiveness of the proposed method.

Maximizing the optimal scheduling capability of a virtual power plant (VPP) over its aggregated resources is crucial for increasing its revenue. However, the limited dispatchable resources in single-energy VPPs hinder maximum economic efficiency. To address this issue, in this paper, a multienergy virtual power plant (MEVPP), which aggregates distributed electrical, thermal, and demand-side flexible resources, is introduced. Furthermore, a low-carbon economic dispatch strategy model is proposed for the coordinated operation of the MEVPP with shared energy storage. First, an MEVPP model incorporating shared energy storage is constructed, with equipment modeling developed from both electrical and thermal dimensions. Second, a low-carbon dispatch strategy that incorporates multiple types of demand responses is formulated, accounting for the effects of electrical and thermal demand responses, as well as carbon emissions, on dispatch. The simulation results demonstrate that, compared with models that do not consider the multienergy demand response, the proposed model reduces system operating costs to 54.2% and system carbon emissions to 42%. Additionally, the MEVPP can leverage energy storage by charging during low-price periods and discharging during high-price periods, thereby enabling low-carbon and economically viable system operation. This study offers valuable insights for the optimized operation of MEVPP systems.

The electricity–hydrogen–electricity conversion chain offers an effective solution for integrating clean energy into the grid while addressing multiple grid control requirements. Moreover, multiregional, interconnected, and integrated energy systems (IESs) can significantly increase overall energy utilization efficiency and operational flexibility through spatiotemporal coordination among diverse energy sources. However, few researchers have considered these two aspects in a unified framework. To address this gap, a low-carbon economic dispatch model and control strategy for a multiregional hydrogen-blended IES are proposed in this work. The model is constructed based on a system architecture that incorporates electricity–hydrogen–electricity conversion links while accounting for source–load uncertainties and peak shaving requirements. We solve the resulting distributed nonconvex nonlinear optimization problem using the alternating direction method of multipliers (ADMM). Furthermore, we analyze how uncertainty factors and peak shaving needs affect the maximum allowable hydrogen blending ratio in the gas grid, as well as the corresponding dynamic blending strategy. Our findings demonstrate that the proposed multiregional hydrogen-blended integrated energy system, with dynamic hydrogen blending control, significantly enhances the capacity for clean energy integration and reduces carbon emissions by approximately 12.3%. The peak-shaving demand is addressed through a coordinated mechanism involving electrolyzers (ELs), gas turbines (GTs), and hydrogen fuel cells (HFCs). This coordinated mechanism enables hydrogen fuel cells to double their output during peak hours, while electrolyzers increase their power consumption by approximately 730 MW during off-peak hours. The proposed dispatch model employs conditional risk measures to quantify the impacts of uncertainty and uses economic coefficients to balance various cost components. This approach enables effective coordination among economic objectives, risk management, and system performance (including peak shaving capability), thereby improving the practical applicability of the model.

The conventional multi-stacked single-ended primary inductor converter (SEPIC)-based voltage equalizer has significant limitations, including excessive current stress on energy storage cells. Moreover, it fails to deliver effective voltage balancing for long ultra-capacitor (UC) strings, severely restricting its practical applicability in large-scale systems. To improve the scalability, a symmetrical multi-stacked SEPIC equalizer for series-connected UC strings is proposed. A symmetrical SEPIC converter for UC balancing can be utilized in this system, which can significantly reduce the current stress typically and reduce the voltage current. Meanwhile, multi-winding transformers are used to independently drive each symmetrical multi-stacked SEPIC, thereby reducing current stress on the balancing circuit and lowering voltage stress in the transfer capacitance. The experiment validated the performance of the proposed equalizer across eight energy storage cells. The results indicated that the proposed voltage equalizer effectively achieved the balancing function, with a system efficiency of 91.19% when the individual UC cell voltage was 2.7 V.

Scenario generation and reduction based on a single variable (e.g., photovoltaic power or load forecasting) is a mainstream approach in current power system planning. However, such methods often overlook the temporal correlation between source and load, which can compromise the credibility of the generated scenarios and lead to suboptimal planning outcomes. To address this issue, this paper proposes an integrated model for microgrid energy storage planning and operation that explicitly considers the joint distribution of source–load scenarios. First, a comprehensive similarity metric is developed by combining dynamic time warping (DTW) distance, slope distance, and source–load correlation distance. An improved K-medoids clustering algorithm is then employed to cluster the joint source–load time series, generating a set of typical scenarios that effectively preserve the coupling characteristics between photovoltaic generation and load demand. Subsequently, a bi-level optimization model is formulated, with energy storage capacity as the primary decision variable. The upper-level planning problem aims to maximize the return on investment (ROI) under energy storage investment constraints, determining the optimal capacity configuration. The lower-level operational problem maximizes the daily net revenue by optimizing the charging and discharging strategies of the energy storage system. Through iterative interaction between the two levels, the model achieves optimal coordination between investment decisions and economic dispatch. Case studies on a campus microgrid demonstrate that the proposed joint scenario generation method effectively captures the temporal correlation between source and load, enhancing both the credibility of the scenarios and the economic rationality of the integrated planning and operation framework.