Enhancing Asset Efficiency of Active Distribution Networks Based on Maximum Power Supply Capacity Analysis

Active Distribution Networks (ADNs) are modern and intelligent power distribution systems that integrate renewable energy sources and advanced control technologies to provide reliable, secure, and efficient electricity supply. The traditional power distribution networks are designed to deliver electricity in a one-way flow from the transmission system to the customers. In contrast, ADN enables two-way power flow between the distribution network and customers, allowing them to become active participants in the electricity system. The key technology behind ADN includes advanced metering infrastructure (AMI), Distribution Automation (DA), and energy storage systems (ESS). AMI provides real-time data on electricity consumption, generation, and distribution, enabling efficient load management and demand response. Other key technologies of ADN include renewable energy sources such as solar photovoltaic (PV), Wind turbines and small-scale hydroelectricity. These sources are connected to the distribution network through power electronic interfaces that convert the DC output of the renewable sources into AC for distribution. Smart grid technologies such as microgrids, virtual power plants, and energy management systems are also integrated into ADN to improve grid stability and resilience. ADNs have the potential to transform the electricity system by enabling more efficient, sustainable, and reliable energy systems that can better serve the needs of consumers and society. Thus, ADN is expected to become a critical technology in achieving a cleaner and more sustainable energy future.

Distributed energy resources transform the passive distribution networks into active distribution networks (ADNs). Coordinating the dispatch actions of distributed resources has been studies in the literature, both within an ADN and between an ADN and the transmission system. However, the direct coordination between ADNs interconnected via a physical tie line is a topic boldly under-discussed, despite its practical relevance. In this paper, we consider the problem of coordinating the dispatch actions, including the energy exchange, of two interconnected ADNs, each one integrated with flexible loads (managed by demand response aggregators), energy storage systems, and microturbines (MTs). The bilateral energy trading enables the neighboring ADNs to benefit from the difference in locational marginal prices. The coordination problem is formulated as a robust bi-level program under price uncertainty. At the upper level, the total cost of the ADNs is minimized subject to the uncertainty of electricity market prices and technical constraints of the networks and the resources. At the lower level, the DR aggregators present at each ADN selfishly minimize their own cost. Moreover, the worst case realization of wholesale electricity market prices is considered. The problem is linearized using the Karush–Kuhn–Tucker (KKT) conditions and decomposed using the alternating direction method of multipliers (ADMM). Simulation results verify the convergence behavior of the proposed method and quantify the value of DSO-DSO coordination in the presence of an interconnecting line between the ADNs.

The maximum power supply capability of distribution network, is an important index for evaluation of the effect of distribution network planning, and in recent years in a number of city of our country distribution network planning, construction and renovation in the process to obtain the effective application of the current distribution network. The calculation method of the maximum power supply capacity of the mainstream, mainly for the meter and the distribution network planning N-1 safety standards, and precisely considered various calculation methods of distribution network operation constraint conditions and distribution network topology, and around this calculation method the results of a series of maximum power supply capability of distribution network properties of solutions, such as applied research, and development of smart grid technology application for the study of maximum power supply capability of distribution network to create the conditions. Under this background, this paper studies on the maximum power supply capacity of smart distribution network are summarized.

The large-scale integration of distributed photovoltaics (PV) can cause reverse power flow due to an imbalance between load and PV generation, altering the voltage profile in traditional distribution networks. The uncertainties in PV output can also cause voltage violations in the distribution network. Conventional methods for assessing static stability in voltage within such systems utilize continuous power flow techniques to calculate the load margin index, which provides a measure of how stable the voltage is in the distribution network. Nevertheless, the frequent load margin calculations in such methods result in significant computational costs, rendering them impractical for networks with distributed generators (DGs). Conventional static voltage stability assessment methods typically use continuation power flow (CPF) to calculate load margins but face significant computational bottlenecks in active distribution networks with distributed generation. For instance, even with improved power flow algorithms, a 33-bus system requires 58 iterations and about 0.3–0.5 seconds per analysis; in large-scale systems with tens of thousands of buses, total evaluation time can exceed 10 minutes. This paper introduces the holomorphic embedding load flow method (HELM), which accommodates different bus types (PI, PQ, PV) in active distribution networks. Building on HELM, a voltage partial sensitivity method is proposed, providing a criterion for evaluating the voltage stability in distribution networks. The sensitivity-based stability criteria using holomorphic embedding functions consider various DG operating conditions without requiring maximum load margin computation. The efficient calculation of these indexes makes the approach ideal for managing voltage in systems that include distribution networks with DGs. Finally, these indexes are used to manage voltage in active distribution networks, and the effectiveness of the method is confirmed through comprehensive numerical examples.

The large-scale integration of high-penetration photovoltaic (PV) generation has exacerbated the curtailment problem in distribution networks, severely restricting the effective utilization of PV power. Unlocking the regulation potential of flexible and controllable resources has become a critical approach to enhancing system accommodation capability and reducing PV curtailment. This paper focuses on distribution networks with high PV penetration and investigates the operational characteristics and regulation potential of flexible resources, including PV inverters, static var generators (SVGs), energy storage systems (ESSs), and electric vehicle battery swapping stations (BSSs). On this basis, a coordinated optimal scheduling model is established with the dual objectives of maximizing PV accommodation and minimizing regulation cost. The proposed model is solved using a genetic algorithm. Simulation results demonstrate that the proposed scheduling strategy effectively exploits the complementary characteristics of multiple types of flexible resources, significantly alleviates PV curtailment, and achieves cost reduction while enhancing PV accommodation.

In order to meet the high permeability and the access of large scale distributed generation, at present and in the future, active distribution network becomes the trend of smart grid research. This paper introduces the influence of distribution network voltage distribution and stability of distributed energy access based on active distribution network; The reactive power and voltage optimization control method of active power distribution network is summarized from the aspects of optimal voltage regulation measure, voltage regulation measure of power network equipment, reactive power optimal dispatch of power system, and so on. Analysis of the current status of the research, and summarized the existing active distribution network with a distributed power pickup distribution network reactive power and voltage control of the existence of some problems and the insufficiency still some problems that must be solved in the study of reactive power and voltage optimization control of active distribution network are pointed out.

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.

In view of the lack of discussion on the connection mode of double-loop network and the influence on the power supply capacity of distribution network in the current soft open point (SOP) planning method, this paper proposes a four-terminal SOP optimization configuration method for active distribution network considering the influence of power supply capacity. Firstly, the application scenario of four-terminal SOP and its control mode under normal and fault operation of distribution network are discussed. Secondly, a bi-level programming method for four-terminal SOP including planning and operation of active distribution network is proposed. On this basis, a planning model aiming at minimizing the sum of the annual investment and operation cost of the four-terminal SOP, the annual normal operation cost of the distribution network and the annual benefit cost taking into account the variation of power supply capacity is constructed. Thirdly, the hybrid integer nonlinear programming model is solved by combining particle swarm optimization, cone programming and power supply capability evaluation. Finally, an example is given to verify the validity and rationality of the proposed method.

This paper proposes the integration and operation of lithium-ion battery energy storage systems (ESS) in active distribution networks with high penetration of distributed generation based on renewable energy. The goal is to minimize total system costs, including energy purchasing at the substation node, as well as ESS integration, maintenance, and replacement costs over a 20-year planning horizon. The proposed master–slave methodology uses the Salp Swarm Optimization Algorithm to determine ESS location, technology, and daily operation schemes, combined with a successive approximation power flow to compute the objective function value and enforce constraints. This approach employs a discrete–continuous encoding, reducing processing times and increasing the likelihood of finding the global optimum. Validated on a 33-node test system adapted to Medellín, Colombia, the methodology outperformed five metaheuristic algorithms, achieving the highest annual savings (USD 16,605.77), the lowest average cost (USD 2,964,139.99), and the fastest processing time (345.71 s). The results demonstrate that the proposed methodology enables network operators to reduce distribution network costs effectively, offering high repeatability, solution quality, and computational efficiency.

Recent years have witnessed the boom of plug-in electric vehicles (PEVs) and the charging facilities. This paper proposes an approach for the co-planning of the electric vehicle charging stations (EVCSs) and energy storage systems (ESSs) in the distribution network. Unlike previous works, each unit for investment is assumed to be a pair of EVCS and ESS and the charging loads are considered flexible. A two-stage stochastic programming model, with the investment problem as the first stage and optimal operation problems of EVCSs and ESSs as the second stage, is formulated by generating representative scenarios of base load and distributed generations. To deal with heavy computational burden, the generalized Benders decomposition (GBD) algorithm is then adopted. The effectiveness of the methodology is demonstrated by the conducted numerical experiments.

Peer-to-peer (P2P) energy trading has emerged as a novel approach to enhancing the coordination and utilization of distributed energy resources (DERs) within modern power distribution networks. This study presents a techno-economic analysis of different DER characteristics, focusing on the integration of photovoltaic (PV) systems and energy storage systems (ESS) within a community-based P2P energy trading framework in Aswan, Egypt, under a time-of-use (ToU) electricity tariff. Eight distinct cases are evaluated to assess the impact of different DER characteristics on P2P energy trading performance and an unbalanced low-voltage (LV) distribution network by varying the PV capacity, ESS capacity, and ESS charging power. To the best of the authors’ knowledge, this is the first study to comprehensively examine the effects of different DER characteristics on P2P energy trading and the associated impacts on an unbalanced distribution network. The findings demonstrate that integrating PV and ESS can substantially reduce operational costs—by 37.19% to 68.22% across the analyzed cases—while enabling more effective energy exchanges among peers and with the distribution system operator (DSO). Moreover, DER integration reduced grid energy imports by 30.09% to 63.21% and improved self-sufficiency, with 30.10% to 63.21% of energy demand covered by community DERs. However, the analysis also reveals that specific DER characteristics—particularly those with low PV capacity (1.5 kWp) and high ESS charging rates (e.g., ESS 13.5 kWh with 2.5 kW inverter)—can significantly increase transformer and line loading, reaching up to 19.90% and 58.91%, respectively, in Case 2. These setups also lead to voltage quality issues, such as increased voltage unbalance factors (VUFs), peaking at 1.261%, and notable phase voltage deviations, with the minimum Vb dropping to 0.972 pu and maximum Vb reaching 1.083 pu. These findings highlight the importance of optimal DER sizing and characteristics to balance economic benefits with technical constraints in P2P energy trading frameworks.

The widespread installation of distributed generation systems is crucial for making optimal use of renewable energy. However, local distribution networks face voltage fluctuation problems if numerous photovoltaic (PV) systems are connected. Recently, energy storage systems that can be installed at commercial customers have been developed. This paper proposes a concept that solves the voltage fluctuation problem in distribution networks with high penetration of PV systems by using customer-side energy storage systems. The distribution network operator (DNO) is allowed to control the output of the energy storage systems of customers during a specific time period in exchange for a subsidy covering a portion of the initial cost of the storage system. The cost effectiveness of the cooperative operation for both customer and DNO is discussed by numerical simulations based on minute-by-minute solar irradiation data. Our results have clarified the possibilities of making voltage management more economical in distribution networks.

With increasing connection of distributed generations (DGs), the power flow in electric distribution network is no longer unidirectional and the network has become active distribution network. Thus, the connection of DGs in electric distribution networks has created a challenge for distribution network operators due to bidirectional power flow. One of the main technical challenges of an active distribution network is to maintain an acceptable voltage level in the system. This has initiated efforts in using various voltage control strategies to regulate the network voltage profile so that the voltage is maintained at its allowable voltage limits. A number of voltage control methods have been applied to solve voltage control problems associated with the connection of DGs in a distribution system. These voltage control strategies may be classified as decentralized or distributed and centralized or coordinated voltage control. From the literature, a number of coordinated voltage control strategies have been implemented to provide better and faster control to the system. This chapter presents an improved coordinated voltage control method in active electric distribution network by coordinating the three voltage control methods, namely, power factor control, on load tap changer control and generation curtailment control. These voltage control methods are coordinated using fuzzy logic by considering the load bus voltages and DG power as inputs and the voltage control actions as the outputs. The fuzzy logic if-then control rules which are generated to build the fuzzy logic control system are based on the simulations results as well as from the previous works. Results obtained using the fuzzy logic based coordinated voltage control has shown that the voltages are able to be kept within its permissible limits.

Distributed energy resource (DER) including wind power, solar energy and energy storage system (ESS) are connected to the active distribution network (ADN) in various combination ways, which makes the distribution network have interaction. As a bridge connecting the transmission grid (TG) and micro grid (MG), ADN breaks the traditional operation pattern of TG+ADN+MG. Considering the physical connections and shared information among TG, ADN and MG, this paper proposes a decentralized and parallel analytical target cascading (ATC) algorithm for TG+ADN+MG multi-level co-optimization dispatch considering primary and secondary frequency regulation. To explore the synergistic ability of the TG+ADN+MG coping with uncertainties of DER, i.e., wind power, the primary and secondary frequency regulation of TG are implemented to cope with uncertainties. Furthermore, the distributional uncertainties of wind power and load are well modeled by data driven, which is proposed in our previous work [1]. An improved 6-bus system is used to test the proposed model, the numerical results illustrate the effectiveness and efficiency of the proposed method.

Energy storage system utilisation to increase photovoltaic penetration in low voltage distribution feeders