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Thanks to the rapid development of photovoltaic (PV) and the popularization of energy storage, PV energy storage systems have become an important part of modern energy systems due to their clean, efficient and renewable characteristics. To solve the problem of optimal allocation of PV energy storage systems in active distribution networks, this study takes the planning cost as the upper objective, sets the operating cost and voltage deviation as the lower objectives, proposes the voltage stability siting constraints, and establishes an optimization model for the capacity allocation of PV energy storage systems. To balance the operating cost and voltage deviation of the active distribution network, the study proposes the multi-objective red-billed blue-magpie optimizer (MORBO), which analyzes the set of multi-objective solutions of the lower layer model and selects the compromise solution by using meta-heuristic algorithms for the adaptation of complex problems. Four case studies are set up for comparative analysis, and the experiments show that the proposed method improves the performance of the active distribution network through the synergistic effect of PV and energy storage, with a reduction of voltage deviation by 5.01% and network losses by 4.37%. As a result, the results of this research can provide decision support in the configuration of photovoltaic energy storage systems for active distribution networks. [ABSTRACT FROM AUTHOR]

Copyright of Zhejiang Electric Power is the property of Zhejiang Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the increasing penetration of distributed power sources in the distribution network, active distribution has become the mainstream direction of the power grid in the future. Microgrids and active stations that are slightly smaller than their scale will gradually increase. Whether these subsystems can be used as energy to schedule becomes the key to improving the economy and stability of power grid operation. Therefore, aiming at the overall optimal scheduling of microgrid and active station area with distributed power supply, a scheduling model solution method based on improved target cascade method was proposed, which mainly takes the optimal benefits generated by different stakeholders as the final scheduling target, and adopts the opportunity constraint description for the processing of uncertain factors of wind and solar. The upper layer is the distribution network and the optimal target of the distribution network, and the lower layer is the microgrid and active station area with the ability to participate in the scheduling. Based on the modeling of distribution network and microgrid, the improved target cascade method was introduced, and the interactive power was used as a shared variable to equivalent the generator and virtual load, so as to realize the decoupling and independent optimization of the upper and lower layers. The comparison of the experimental results shows that the target cascade method with the balance coefficient can obtain better results in the number of iterations, convergence performance, anti-interference performance and overall economic evaluation. [ABSTRACT FROM AUTHOR]

Aiming at solving the problem of new energy and load uncertainty leading to a steep increase in the demand for flexible reserve resources by integrated energy service providers (IESPs), a coordinated and optimized scheduling method for multi-region integrated energy service providers considering flexible reserve resources is proposed. First, for the uncertainty of new energy and load, Latin hypercube sampling is used to generate scenarios, and the scenarios are reduced by a K-means clustering algorithm. Second, based on the interaction relationship between the active distribution network (ADN) and multi-region IESPs, a mixed game model of the ADN and IESP alliance is established. ADN guides IESPs to optimize their operation by setting prices for electricity and reserves, and IESPs fully tap their own flexible reserve resources according to the prices set by ADN and achieve power interoperability through the interaction of IESPs in multiple regions to synergistically cope with the uncertainties of new energy and load. Finally, the example results show that the model proposed in this paper is able to realize the allocation of flexibility resources in a wider range, reduce the reserve pressure on the superior grid, and improve the profitability of IESPs. [ABSTRACT FROM AUTHOR]

In order to optimize the economic operation level of the active distribution network and improve the energy utilization rate, a layered coordinated intelligent control method of source network load–storage for the active distribution network is studied. In this method, a layered coordinated intelligent control model of source network load and storage is established. The upper model takes the minimum annual comprehensive cost as the coordination control goal and optimizes the installation capacity of distributed generation and energy storage systems in the candidate installation locations; the lower level model proposes a peak-shaving and valley-filling operation strategy for the energy storage system, which aims at minimizing the network loss to control the stable operation of the power system; using a dual-objective Pareto solution method based on a modified ε-constraint method, solve the upper and lower control models, obtain the Pareto optimal solution set of available schemes, and then use the fuzzy decision method to extract the optimal location and capacity scheme of distributed generation and energy storage systems that can ensure the stable operation of power systems. After testing, the annual investment cost and maintenance cost of DG and ESS decreased by 5,139,500 yuan and 0.024 yuan, respectively, after using this method; The fluctuation of load power in energy storage systems is significantly reduced. At the same time, the consumption of wind/photovoltaic power has increased from around 1800 MW h to around 3200 MW h, indicating a significant improvement in the consumption of new energy. It has higher economic efficiency and good peak-shaving and valley-filling effects. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Rational allocation of energy storage not only facilitates the new energy consumption but also enables peak shaving and valley filling， ensuring the safe， reliable， and economical operation of distribution networks. This paper proposes an optimal strategy for energy storage allocation that considers new energy consumption rates， establishing a two-layer model for planning and operation. The upper layer employs a grey wolf optimizer （GWO） to determine the locations， capacities， and power ratings of energy storage， which are then passed to the lower layer as newly dispatchable energy storage resources. The lower layer uses an economical operation model for active distribution networks based on second-order cone dynamic power flow to solve the minimal total operational costs. The investment costs for energy storage are incorporated as a fitness function， providing feedback to the upper layer to guide the iterative optimization. Case studies using the modified IEEE 33-bus system demonstrate that the optimal energy storage allocation derived from the proposed strategy effectively increases the consumption rates of wind and solar energy while enhancing the overall economy of the distribution networks.

Due to the increasing microgrid group and shared energy storage integration into active distribution network (ADN), it is necessary to effectively coordinate these complexity energy elements. Therefore, a master-slave game schedule strategy is constructed for ADN based on microgrid group and shared energy storage. The time-of-use electricity price is decided by the ADN as the main body, so the microgrid group and shared energy storage should respond to the electricity price as the subordinate body, which may consider the safe operation and the peak shaving schedule. Moreover, the two-stage power interaction strategy between the microgrid group and shared energy storage is developed by the time-of-use electricity price. In the first stage, the energy storage leasing demand of microgrid group can be calculated through multi-objective optimization algorithms. Then, the charging and discharging strategy is formulated for the shared energy storage which can meet the power demand of the microgrid group and respond to distribution network schedule by the remaining capacity. In the second stage, a schedule strategy is formulated for the cooperative alliance considering power interaction among microgrids and a mechanism of benefit allocation. Furthermore, the equilibrium solution of the master-slave game may be solved through the Quantum Particle Swarm Optimization (QPSO) algorithm nested with the cplex solver. At last, the effectiveness and rationality of the proposed strategy can be verified by the improved IEEE33 bus system. • A master-slave game schedule strategy is proposed for ADN based on microgrid group and SES to solve the problem of pricing and optimization in multi-entity game. • The innovative multi-objective energy storage leasing model for microgrids is constructed by introducing the net load mean square deviation and the surplus/shortage power into the optimization objective function. • A multi-stage distributed iterative algorithm is proposed to effectively solve the leasing model and game equilibrium solution, which can avoid nested iterations in the solving process. • The benefit allocation mechanisms for MGCO is developed based on the power interaction ratio among microgrids to improve the efficiency and rationality of distribution. [ABSTRACT FROM AUTHOR]

Coordination between virtual power plants and active distribution networks is crucial as these plants increasingly aggregate distributed resources within the power system. This study introduces a bilevel optimization framework to coordinate the scheduling of multiple virtual power plants and an active distribution network using pricing strategies for energy and reserves. The upper-level optimization minimizes total operating costs by incorporating bidding plans of the active distribution network in various markets, its interactions with multiple virtual power plants, and operational costs. The lower-level optimization maximizes revenue for each virtual power plant, considering both battery capacity degradation costs and operational costs of various resources. To facilitate solutions, this research developed a nonlinear transformation method for modeling capacity degradation. Based on the dispatching strategy from the virtual power plant, this study uses the squared difference between energy consumption of equipment for controllable loads and the strategy as the optimization target to derive control strategies for two equipment types. Results show that the framework effectively integrates dispatch and control strategies without oversimplifying the system model, proving its applicability in various scenarios with diverse resource compositions. • A bi-level optimization framework is proposed. • A variety of centrally manageable resources are gathered. • A nonlinear transformation method for easier problem-solving is used. • The framework enables scheduling and control of two controllable loads. [ABSTRACT FROM AUTHOR]

• A MILP black-start model for active distribution networks is developed. • The impact of source-side and load-side uncertainty is considered. • The whole network is optimized directly without prior partitioning. • A "multi-point launch, parallel expansion, and integration" strategy is formed. The integration of multi-type resources provides new ideas for the black-start of active distribution networks (ADNs). However, the inability to deal with uncertainty will lead to problems such as frequency/voltage crossing limits, scheduling difficulties, and even restoration failures. To this end, an ADN black-start strategy considering source-load bilateral uncertainty and multi-type resources is proposed. The forecast error uncertainties of renewable energy sources (RESs) and loads are characterized in intervals based on Copula theory, which are then introduced into the black-start model of ADNs and solved by the column-and-constraint generation algorithm. Case studies based on the improved IEEE 33-node system indicate that the proposed strategy can effectively cope with source-load bilateral uncertainty and realize robust restoration of ADNs. The system also achieves a 99.97 % RESs consumption ratio and a 67.36 % power utilization ratio of energy storage devices. Compared with existing methods, our strategy can give a more economical and faster restoration scheme while considering safety, which can be deployed in dispatch centers to help operators make full use of existing resources to achieve black-start safely and stably after outages. However, the computational time will increase if it is migrated to grids with large topologies, which needs to be further investigated. [Display omitted] [ABSTRACT FROM AUTHOR]

• For the path movement of mobile energy storage system, traffic condition is taken into account which includes various types of routes and congestion levels on different routes. It is more in line with the actual situation. • The concept of virtual switch is introduced to achieve the decoupling of transportation network and active distribution network. The path movement of mobile energy storage system in transportation network is converted to the switching of virtual switch in active distribution network. A coordinated optimal model considering mobile energy storage system and dynamic network reconfiguration can be solved in active distribution network. • To improve the computational efficiency, penalty alternating direction method is utilized to handle the binary variables in the optimization model. The model can be solved in a relatively short time. • A coordinated optimal model including mobile energy storage system and dynamic network reconfiguration is proposed and linearized, and the effectiveness of the optimization strategy can be verified by simulation in the IEEE 33-bus system coupled with the 15-node transportation systems, and the stability of the proposed strategy was validated in a larger scale expansion system. The active distribution network (ADN) can face with challenges due to the increasing renewable distributed generation (RDG), which may result in elevated network losses and voltage fluctuations. To address these issues, a novel operation strategy is proposed which integrates the mobile energy storage system (MESS) and dynamic network reconfiguration (DNR) to adjust the active and reactive power of the ADN. The transportation network (TN) is modeled considering the traffic congestion, and the path movement of MESS in TN is converted to the switching of virtual switch (VS) in ADN. A coordinated optimal model is formulated for DNR and MESS, furthermore, which can be transformed into a mixed-integer second-order cone programming (MISOCP) model. The penalty alternating direction method (PADM) is employed to enhance the computational efficiency. Then the proposed strategy is tested by the IEEE 33-bus system coupled with the 15-node transportation systems, and the stability of the proposed strategy was validated in a larger scale expansion system. The simulation results demonstrate that the coordinated optimal strategy considering MESS and DNR can effectively reduce network loss and transportation cost, enhance the voltage quality of the ADN and promote the consumption of renewable energy. [ABSTRACT FROM AUTHOR]

To address the insufficient online measurement and high complexity in overall modelling of active distribution network (ADN), this paper proposes a key node identification and network simplification modelling method for optimal power flow (OPF) analysis of ADN. The main contributions of this paper are as follows. • Key node identification method and reduction procedure are proposed with comprehensive evaluation index and based on entropy weight method. The proposed evaluation index comprehensively considers the peak and valley values of node voltage and the net load power to identify the key nodes of each cluster. The use of entropy weight method overcomes the subjectivity and unicity of index weight, and the identified key nodes are more representative. Besides, the proposed key node reduction procedure can determine the preferential deletion sequence of key nodes to further reduce the number of measurement devices. • An improved π-type network simplification method is proposed with load displacement principle. The power supply paths between key nodes are efficiently searched by depth-first search algorithm to obtain the upstream and downstream relationships between key nodes. Compared to the existing simplification methods, the improved network simplification method only includes the identified key nodes and the middle virtual load nodes are eliminated through load displacement. Thus, the improved π-type network simplification method can effectively reduce the dimension of simplified power flow model and guarantee accuracy of OPF model. With the increasing scale of active distribution network (ADN), the power flow analysis of ADN faces with many difficulties, such as insufficient online measurement and high complexity of overall modelling. Therefore, this paper proposes a key node identification and network simplification modelling method for the optimal power flow (OPF) analysis of ADN. Firstly, a voltage extremum similarity index is proposed and combined with K-means clustering algorithm to partition the ADN into several clusters, and then a comprehensive evaluation index is constructed with the entropy weight method to identify the key nodes of each cluster. On this basis, a key node reduction procedure is established with the comprehensive index of voltage violation probability to further reduce the scale of key nodes. After that, the power supply paths between key nodes are searched by the depth-first search algorithm to construct the topology of simplified network and then an improved π-type simplification network method is proposed with load displacement principle to establish the simplified network model with low dimension. Finally, the modified IEEE 123-bus system is used to verify the effectiveness and accuracy of proposed method. The simulation results indicate that the proposed key node identification method can accurately identify the voltage extreme nodes including PV connected nodes, and the proposed network simplification method can effectively improve the efficiency of OPF analysis with guaranteed accuracy. [ABSTRACT FROM AUTHOR]