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The approach to planning, design and operation of distribution networks have significantly changed due to the proliferation of distributed energy resources (DERs) together with load growth, energy storage technology advancements and increased consumer expectations. Planning of active distribution systems (ADS) has been a very hot topic in the 21st Century. A large number of studies have been done on ADS planning. This paper reviews the state of the art of current ADS planning. Firstly, the influences of DERs on the ADS planning are addressed. Secondly, the characteristics and objectives of ADS planning are summarized. Then, up to date planning model and some related research are highlighted in different areas such as forecasting load and distributed generation, mathematical model of ADS planning and solution algorithms. Finally, the paper explores some directions of future research on ADS planning including planning collaboratively with all elements combined in ADS, taking into account of joint planning in secondary system, coordinating goals among different layers, integrating detailed operation simulations and regular performance based reviews into planning, and developing advanced planning tools.

An active distribution system power-supply planning model considering cooling, heating and power load balance is proposed in this paper. A regional energy service company is assumed to be in charge of the investment and operation for the system in the model. The expansion of substations, building up distributed combined cooling, heating and power (CCHP), gas heating boiler (GHB) and air conditioner (AC) are included as investment planning options. In terms of operation, the load scenarios are divided into heating, cooling and transition periods. Also, the extreme load scene is included to assure the power supply reliability of the system. Numerical results demonstrate the effectiveness of the proposed model and illustrate the economic benefits of applying distributed CCHP in regional power supply on investment and operation.

Active energy management is an effective way to realize the flexible utilization of distributed energy resources to suit the characteristics of active distribution system. Advanced active energy management strategies need to be designed to coordinate the optimization of ‘generation, network, load’. An active management model is built for the local distribution system integrated with the generation curtailment mechanism and the charging/discharging management of plug-in electric vehicles. Furthermore, different strategies based on the energy management model are presented. The model and strategies are tested and discussed in a modified distribution system, and the impacts with different load profiles are also analyzed.

Due to the widespread deployment of distributed energy resources (DERs) and the liberalization of electricity market, traditional distribution networks are undergoing a transition to active distribution systems (ADSs), and the traditional deterministic planning methods have become unsuitable under the high penetration of DERs. Aiming to develop appropriate models and methodologies for the planning of ADSs, the key features of ADS planning problem are analyzed from the different perspectives, such as the allocation of DGs and ESS, coupling of operation and planning, and high-level uncertainties. Based on these analyses, this comprehensive literature review summarizes the latest research and development associated with ADS planning. The planning models and methods proposed in these research works are analyzed and categorized from different perspectives including objectives, decision variables, constraint conditions, and solving algorithms. The key theoretical issues and challenges of ADS planning are extracted and discussed. Meanwhile, emphasis is also given to the suitable suggestions to deal with these abovementioned issues based on the available literature and comparisons between them. Finally, several important research prospects are recommended for further research in ADS planning field, such as planning with multiple micro-grids (MGs), collaborative planning between ADSs and information communication system (ICS), and planning from different perspectives of multi-stakeholders.

In this paper, we propose a data-driven distributionally robust optimization (DRO) framework that ensures the economical and robust operation of solar photovoltaic (PV)-integrated battery charging swapping stations (BCSSs) for electric vehicles (EVs) under uncertainties in active distribution systems with stand-alone PV systems. In the proposed framework, multiple inventory batteries in each BCSS are used through their charging and discharging real and/or reactive power scheduling to perform Volt/VAR control (VVC) along with stand-alone PV systems, and to reduce the BCSS operational cost via battery-to-battery (B2B)-based real power exchange and demand response (DR) while satisfying the desired EV battery swapping load. To handle the uncertainties in both PV generation outputs and DR-induced maximum demand reduction capability, the proposed framework is formulated as a data-driven DRO problem based on the Wasserstein metric using historical samples of the probability distributions of the uncertainties. Using a duality theory, the original Wasserstein-based DRO problem is reformulated into a tractable optimization problem that calculates the distributionally robust bounds of uncertainties using their support information. The effectiveness of the proposed framework was assessed on an IEEE 33-node power distribution system in terms of real power loss reduction via VVC and BCSS operational cost savings via B2B/DR capability.

Participation of distributed energy resources in the load restoration procedure, known as intentional islanding, can significantly improve the distribution system reliability. Distribution system reconfiguration can effectively alter islanding procedure and thus provide an opportunity to supply more demanded energy and reduce distribution system losses. In addition, high-impact events such as hurricanes and earthquake may complicate the procedure of load restoration, due to disconnection of the distribution system from the upstream grid or concurrent component outages. This paper presents a two-level method for intentional islanding of a reconfigurable distribution system, considering high impact events. In the first level, optimal islands are selected according to the graph model of the distribution system. In the second level, an optimal power flow (OPF) problem is solved to meet the operation constraints of the islands by reactive power control and demand side management. The proposed problem in the first level is solved by a combination of depth first search and particle swarm optimization methods. The OPF problem in the second level is solved in DIgSILENT software. The proposed method is implemented in the IEEE 69-bus test system, and the results show the validity and effectiveness of the proposed algorithm.

Global trends such as the growing share of renewable energy sources in the generation mix, electrification, e-mobility, and the increasing number of prosumers reshape the electricity value chain, and distribution systems are necessarily affected. These systems were planned, developed, and operated as a passive structure for decades with low level of observability. Due to the increasing number of system states, real time operation planning and flexibility services are the key in transition to an active grid management. In this pathway, distribution system state estimation (DSSE) has a great potential, but the real demonstration of this technique is in an early stage, especially on low-voltage level. This paper focuses on the gap between theory and practice and summarizes the limits of low-voltage DSSE implementation. The literature and the main findings follow the general structure of a state estimation process (meter placement, bad data detection, observability, etc.) giving a more essential and traceable overview structure. Moreover, the paper provides a comprehensive mapping of the possible use-cases state estimation and evaluates 27 different experimental sites to conclude on the practical applicability aspects.

Voltage control is becoming a key issue in active distribution systems, which are electric distribution networks characterized by a large penetration of DERs. Traditional voltage control devices, as well as the active and reactive powers injected by DERs, can be used as ancillary services to support voltage profiles along the distribution feeders. Due to the peculiar characteristics of active distribution systems, the decentralized control approach presents the most promising technical and economical features. In the paper, the decentralized voltage control structure is hierarchically decomposed into different control levels, characterized by different objectives and time frames. The primary and secondary control levels have been analyzed, always according to a decentralized approach. For each level, the various techniques for solving the voltage control problem that have been proposed in the literature are presented, and their main features compared. The main open issues related to the real time practical implementation of the decentralized architectures at both primary and secondary voltage control levels are investigated, keeping always in mind both technical and economical aspects, which always represent the components of a trade-off solution.

A fuzzy multi-objective bi-level optimization problem is proposed to model the planning of energy storage system (ESS) in active distribution systems (ADS). The proposed model enables us to take into account how optimal operation strategy of ESS in the lower level can affect and be affected by the optimal allocation of ESS in the upper level. The power characteristic model of micro-grid (MG) and typical daily scenarios are established to take full consideration of time-variable nature of renewable energy generations (REGs) and load demand while easing the burden of computation. To solve the bi-level mixed integer problem, a multi-subgroup hierarchical chaos hybrid algorithm is introduced based on differential evolution (DE) and particle swarm optimization (PSO). The modified IEEE-33 bus benchmark distribution system is utilized to investigate the availability and effectiveness of the proposed model and the hybrid algorithm. Results indicate that the planning model gives an adequate consideration to the optimal operation and different roles of ESS, and has the advantages of objectiveness and reasonableness.

The increase in the use of converter-interfaced generators (CIGs) in today’s electrical grids will require these generators both to supply power and participate in voltage control and provision of grid stability. At the same time, new possibilities of secondary QU droop control in power grids with a large proportion of CIGs (PV panels, wind generators, micro-turbines, fuel cells, and others) open new ways for DSO to increase energy flexibility and maximize hosting capacity. This study extends the existing secondary QU droop control models to enhance the efficiency of CIG integration into electrical networks. The paper presents an approach to decentralized control of secondary voltage through converters based on a multi-agent reinforcement learning (MARL) algorithm. A procedure is also proposed for analyzing hosting capacity and voltage flexibility in a power grid in terms of secondary voltage control. The effectiveness of the proposed static MARL control is demonstrated by the example of a modified IEEE 34-bus test feeder containing CIGs. Experiments have shown that the decentralized approach at issue is effective in stabilizing nodal voltage and preventing overcurrent in lines under various heavy load conditions often caused by active power injections from CIGs themselves and power exchange processes within the TSO/DSO market interaction.