In this paper, a cost-benefit analysis based optimal planning model of battery energy storage system (BESS) in active distribution system (ADS) is established considering a new BESS operation strategy. Reliability improvement benefit of BESS is considered and a numerical calculation method based on expectation is proposed for simple and convenient calculation of system reliability improvement with BESS in planning phase. Decision variables include both configuration variables and operation strategy control variables. In order to prevent the interaction between two types of variables and enhance global search ability, intelligent single particle optimizer (ISPO) is adopted to optimize this model. Case studies on a modified IEEE benchmark system verified the performance of the proposed operation strategy and optimal planning model of BESS.

Up till now, the high penetration of intermittent distributed generation (DG) has posed great challenges to the planning and operation of the grid. To achieve the best balance between economic cost and acceptable capacity of DG, this paper proposes a new integrated planning method of the active distribution network while considering voltage control cost. Firstly, characteristics of decentralized and centralized voltage control methods were analyzed. The technical frameworks, voltage control strategies and economical models of different voltage control systems were put forward. Then, an integrated planning model with objectives to minimize the comprehensive cost and maximize clean energy utilization under the constraint of maintaining acceptable voltage was implemented. Simulations were conducted using the Multi-objective Differential Evolution Algorithm (MODE). IEEE 33-bus test systems were employed to verify the effectiveness of the proposed method. The results demonstrate that the proposed approach is able to connect larger distributed generators and decrease the economic cost of Distribution Network Operators while maintaining voltage within the statutory limits.

This paper presents the concept of the generalized power source in an active distribution network. After the energy storage system (ESS), distributed generators (DG), and demand-side controllable load are connected to the active distribution network, part of the system load can be undertaken by these ESS, DG, and controllable load instead of relying on the capacity of the substation, which greatly improves the reliability of the system. The above-mentioned distributed energy resources in the active distribution network are collectively referred to as the generalized power source (GPS). Substation credible capacity refers to the capacity of the GPS to supply the distribution network that is equivalent at the high voltage distribution network level when considering the faults of the main transformer, 110 kV lines, and circuit breakers. Considering the uncertainties and control strategies of GPS, this paper takes a specific distribution area as the background to study its substituted substation capacity. Based on a sequential Monte Carlo evaluation framework and the principle of constant reliability, an evaluation method of the GPS credible capacity is proposed. In order to verify the effectiveness of the method proposed in this paper, the credible capacity of various typical connection modes of a high voltage distribution network is quantitatively analyzed in the case study, which can provide a reference for the capacity planning of an active distribution network substation and grid structure selection.

Compared to electric vehicle (EV) charging mode, battery swapping mode can realize concentrated and orderly charging. Therefore battery swapping stations (BSS) can participate in the demand side management (DSM) as an integrated form. In this context, a new method to plan the capacity and location of BSS for EV, considering DSM, is proposed in this paper. Firstly, based on the original charging power of BSS with the rule of "First-In First-Out", a bi-level optimal configuration model of BSS, in which net profit of BSS is maximized in the upper model and operating cost of Distribution Company is minimized in the lower model, is developed to decide the rated power, number of batteries, contract pricing and dispatched power of BSS for DSM. Then, the optimal locating model of BSS with the objective of minimizing network loss is built. A mesh adaptive direct search algorithm with YALMIP toolbox is applied to optimize the bi-level model. Simulation calculation was carried on IEEE-33 nodes distribution system and the results show that participating in DSM can improve the economic benefits of both BSS and distribution network and promote the consumption of distributed generation, verifying the feasibility and effectiveness of the proposed method.

The large scale deployment of renewable generation is generally seen as the most promising option for displacing fossil fuel generators. A challenge in integrating renewable energy resources (RERs) for distribution networks is to find approaches that ensure the long term sustainability and economic profit of the Distribution Company (DisCo). In this paper, considering the air condition load demand side response, a coordinate optimization of the energy storage capacity and operation strategy is presented to maximize the economic profit of the DisCo. The operation strategy in the optimization is divided into two parts. Under the normal state, a price-based air condition quick response strategy is proposed, with both the comfort and economic efficiency of the users taken into account. Under the fault state, a sharing strategy of Generalized Demand Side Resources (GDSRs) is proposed to improve the utilization level of equipment based on the reliability insurance. Finally, the optimization is carried out on an improved IEEE-33 bus test system. The simulation results verify the effectiveness of the proposed method and discuss the effect of the load demand response participation rate on energy storage configuration. At the same time, the effect of GDSRs on the safe load rate of the line is also presented. The research provides a reference for the optimization and utilization of GDSRs.