Search Results (32)

With the deep integration of electricity and carbon trading markets, distribution networks are facing growing operational stress and a shortage of flexible resources under high penetration of renewable energy. This paper proposes a three-layer coordinated planning model for Source–Grid–Load–Storage (SGLS) systems, considering electricity–carbon coupling and flexibility supply–demand balance. The model incorporates a dynamic pricing mechanism that links carbon pricing and time-of-use electricity tariffs, and integrates multi-source flexible resources—such as wind, photovoltaic (PV), conventional generators, energy storage systems (ESS), and controllable loads—to quantify the system’s flexibility capacity. A hierarchical structure encompassing “decision–planning–operation” is designed to achieve coordinated optimization of resource allocation, cost minimization, and operational efficiency. To improve the model’s computational efficiency and convergence performance, an improved adaptive particle swarm optimization (IAPSO) algorithm is developed which integrates dynamic inertia weight adjustment, adaptive acceleration factors, and Gaussian mutation. Simulation studies conducted on the IEEE 33-bus distribution system demonstrate that the proposed model outperforms conventional approaches in terms of operational economy, carbon emission reduction, system flexibility, and renewable energy accommodation. The approach provides effective support for the coordinated deployment of diverse resources in next-generation power systems. Full article

High permeability distributed photovoltaic (PV) access to the distribution network makes it easy to cause frequent overvoltage of the system. However, the traditional centralized optimization scheduling method is difficult to meet the real-time voltage regulation requirements due to high communication costs. In this regard, this paper proposes a distributed fast voltage regulation method for energy storage systems (ESSs) in distribution networks. Firstly, to reduce the communication burden, the distribution network cluster is divided according to the electrical distance modularity index. Secondly, the distributed control model of active distribution network with the goal of voltage recovery and ESS power balance is established, and a distributed controller is designed. The feedback-control gains are optimized to improve the convergence rate. Finally, the IEEE33 bus system and IEEE69 bus system are applied for simulation. The results show that the proposed distributed optimal control method can effectively improve the voltage level of the distribution network under the condition of ensuring the ESS power balance. Full article

To address the impacts of source load temporal–spatial uncertainties on distribution network planning considering the global transition towards sustainable energy systems with high-penetration photovoltaic (PV) integration, this paper proposes a source–network–storage coordinated stochastic planning method. A temporal–spatial correlation probability model for PV output and load demand is constructed based on Copula theory. Scenario generation and efficient reduction are achieved through Monte Carlo sampling and K-means clustering, extracting representative daily scenarios that preserve the temporal–spatial characteristics. A coordinated planning model targeting the minimization of comprehensive costs is established to holistically optimize PV deployment, energy storage system (ESS) configuration, and network expansion schemes. Simulations on typical distribution network systems demonstrate that the proposed method, by integrating temporal–spatial correlation modeling and multi-element collaborative decision-making, significantly improves PV accommodation capacity and reduces planning costs while improving the overall economic efficiency of distribution network planning. This study provides a robust technical pathway for developing economically viable and resilient distribution networks capable of integrating large-scale renewable energy, thereby contributing to the decarbonization of the power sector and advancing the goals of sustainable energy development. Full article

Virtual inertia is a measure of the capability of distributed sources and loads within power supply units to resist system frequency variations through additional control strategies applied to converters. The reasonable allocation of virtual inertia is beneficial for enhancing system stability. In response to the insufficient consideration of multi-regional coordination and difficulties in balancing frequency change rates in existing virtual inertia allocation methods, this paper proposes a neural network-based coordinated virtual inertia allocation method for multiple regions. First, a data-driven model is constructed based on the RBFNN neural networks to map the feasible region boundaries of virtual inertia for distributed resources under different disturbance scenarios. Second, a multi-area virtual inertia optimization allocation model is established, aiming to minimize both the inter-area frequency change rates and the differences between them, while considering the regulation capabilities of grid-forming PV systems and ESS. Following this, a genetic algorithm-based solving strategy is designed to achieve the global optimal allocation of virtual inertia. Finally, simulations verify the effectiveness of the coordinated allocation strategy in enhancing frequency stability across multiple autonomous regions. This optimization method reduces the frequency variation rate in both regions and maintains relative stability between the regions, thereby enhancing the system’s disturbance rejection capability. The results showed that after optimizing the virtual inertia allocation using the method proposed in this paper, the frequencies of the two regions increased by 0.11 Hz and 0.14 Hz, respectively, and the dynamic rate of frequency change decreased by 50.2% and 52.1%. Therefore, this study provides a foundational method and a feasible approach to multi-area virtual inertia optimization allocation in the new distribution system, contributing to frequency support via virtual inertia in distribution network optimization operation. Full article

Conventional approaches for distributed generation (DG) planning often fall short in addressing operational demands and regional control requirements within distribution networks. To overcome these limitations, this paper introduces a cluster-oriented DG planning method. In terms of cluster partitioning, this study breaks through the limitations of traditional methods that solely focus on electrical parameters or single functions. Innovatively, it partitions the distribution network by comprehensively considering multiple critical factors such as system grid structure, nodal load characteristics, electrical coupling strength, and power balance, thereby establishing a unique multi-level grid structure of **distribution network—cluster—node**. This partitioning approach not only effectively reduces inter-cluster reactive power transmission and enhances regional power self-balancing capabilities but also lays a solid foundation for the precise planning of subsequent distributed energy resources. It represents a functional expansion that existing cluster partitioning methods have not fully achieved. In the construction of the planning model, a two-layer coordinated siting and sizing planning model for distributed photovoltaics (DPV) and energy storage systems (ESS) is proposed based on cluster partitioning. In contrast to traditional models, this model for the first time considers the interaction between power source planning and system operation across different time scales. The upper layer aims to minimize the annual comprehensive cost by optimizing the capacity and power allocation of DPV and ESS in each cluster. The lower layer focuses on minimizing system network losses to precisely determine the PV connection capacity of each node within the cluster and the grid connection locations of ESS, achieving comprehensive optimization from macro to micro levels. For the solution algorithm, a two-layer iterative hybrid particle swarm algorithm (HPSO) embedded with power flow calculation is designed. Compared to traditional single particle swarm algorithms, HPSO integrates power flow calculations, allowing for a more accurate consideration of the actual operating conditions of the power grid and avoiding the issue in traditional methods where the current and voltage distribution are often neglected in the optimization process. Additionally, HPSO, through its two-layer iterative approach, is able to better balance global and local search, effectively improving the solution efficiency and accuracy. This algorithm integrates the advantages of the particle swarm optimization algorithm and the binary particle swarm optimization algorithm, achieving iterative solutions through efficient information exchange between the two layers of particle swarms. Compared with conventional particle swarm algorithms and other related algorithms, it represents a qualitative leap in computational efficiency and accuracy, enabling faster and more accurate handling of complex planning problems. Case studies on a real 10 kV distribution network validate the practicality of the proposed framework and the robustness of the solution technique. Full article

With the increasing penetration of distributed photovoltaic-energy storage system (PV-ESS) access distribution networks, the safe and stable operation of the system has brought a huge impact, in which the voltage regulation of PV-ESS distribution networks is more prominent. This paper comprehensively reviews the voltage over-run mechanism in the PV-ESS distribution network and combs through the current mainstream voltage regulation strategies, of which two strategies of direct voltage regulation and current optimization are summarized. At the same time, this paper discusses the advantages and limitations of centralized, distributed, multi-timescale, voltage-reactive joint optimization and other regulation methods and focuses on the analysis of heuristic algorithms and algorithms based on deep reinforcement learning in the voltage regulation of the relevant research progress. Finally, this paper points out the main challenges currently facing voltage regulation in PV-ESS distribution networks, including cluster dynamic partitioning technologies, multi-timescale control of hybrid voltage regulation devices, and synergistic problems of demand-side resources, such as electric vehicle participation in voltage regulation, etc., and gives an outlook on future research directions. The aim of this paper is to provide a theoretical basis and practical guidance for voltage regulation of PV-ESS distribution networks and to promote the intelligent construction and sustainable development of power grids. Full article

This paper presents a comprehensive optimization framework for modern distribution systems, integrating distribution system reconfiguration (DSR), soft open point (SOP) operation, photovoltaic (PV) allocation, and energy storage system (ESS) management to minimize daily active power losses. The proposed approach employs a novel hybrid metaheuristic algorithm, the Cheetah-Grey Wolf Optimizer (CGWO), which synergizes the global exploration capabilities of the Cheetah Optimizer (CO) with the local exploitation strengths of Grey Wolf Optimization (GWO). The optimization model addresses time-varying loads, renewable generation profiles, and dynamic network topology while rigorously enforcing operational constraints, including radiality, voltage limits, ESS state-of-charge dynamics, and SOP capacity. Simulations on a 33-bus distribution system demonstrate the effectiveness of the framework across eight case studies, with the full DER integration case (DSR + PV + ESS + SOP) achieving a 67.2% reduction in energy losses compared to the base configuration. By combining the global exploration of CO with the local exploitation of GWO, the hybrid CGWO algorithm outperforms traditional techniques (such as PSO and GWO) and avoids premature convergence while preserving computational efficiency—two major drawbacks of standalone metaheuristics. Comparative analysis highlights CGWO’s superiority over standalone algorithms, yielding the lowest energy losses (997.41 kWh), balanced ESS utilization, and stable voltage profiles. The results underscore the transformative potential of coordinated DER optimization in enhancing grid efficiency and reliability. Full article

Optimizing the operation of active distribution networks (ADNs) has become more challenging because of the uncertainty created by the high penetration level of distributed photovoltaic (PV). From the convex optimization perspective, this paper proposes a two-layer optimization model to simplify the solution of the ADN optimal operation problem. Firstly, to pick out the ADN “key” nodes, a “key” nodes selection approach that used improved K-means clustering algorithm and two indexes (integrated voltage sensitivity and reactive power-balance degree) is introduced. Then, a two-layer ADN optimization model is built using various time scales. The upper layer is a long-time-scale model with on-load tap-changer transformer (OLTC) and capacitor bank (CB), and the lower layer is a short-time-scale optimization model with PV inverters and distributed energy storages (ESs). To take into account the PV users’ interests, maximizing PV active power output is added to the objective. Afterwards, under the application of the second-order cone programming (SOCP) power-flow model, a linearization method of OLTC model and its tap change frequency constraints are proposed. The linear OLTC model, together with the linear models of the other equipment, constructs a mixed-integer second-order cone convex optimization (MISOCP) model. Finally, the effectiveness of the proposed method is verified by solving the IEEE33 node system using the CPLEX solver. Full article

The increasing integration of renewable energy-based distributed generation (DG) in modern distribution networks is essential for reducing reliance on fossil fuels. However, the unpredictability and intermittency of renewable sources such as wind and photovoltaic (PV) systems introduce significant challenges for distribution network planning. To address these challenges, this paper proposes a Q-learning-based Distributionally Robust Optimization (DRO) model for expansion planning of distribution networks and generation units. The proposed model incorporates energy storage systems (ESSs), renewable DG, substations, and distribution lines while considering uncertainties such as renewable generation variability, load fluctuations, and system contingencies. Through a dynamic decision-making process using Q-learning, the model adapts to changing network conditions to minimize the total system cost while maintaining reliability. The Latin Hypercube Sampling (LHS) method is employed to generate multi-scenario data, and piecewise linearization is used to reduce the computational complexity of the AC power flow equations. Numerical results demonstrate that the model significantly improves system reliability and economic efficiency under multiple uncertainty scenarios. The results also highlight the crucial role of the ESS in mitigating the variability of renewable energy and reducing the expected energy not supplied (EENS). Full article

Configuring energy storage systems (ESSs) in distribution networks is an effective way to alleviate issues induced by intermittent distributed generation such as transformer overloading and line congestion. However, flexibility has not been fully taken into account when placing ESSs. This paper proposes a novel ESS placement method for flexible interconnected distribution networks considering flexibility constraints. An ESS siting and sizing model is formulated aiming to minimize the life-cycle cost of ESSs along with the annual network loss cost, electricity purchasing cost from the upper-level power grid, photovoltaic (PV) curtailment cost, and ESS scheduling cost while fulfilling various security constraints. Flexible ramp-up/-down constraints of the system are added to improve the ability to adapt to random changes in both power supply and demand sides, while a fluctuation rate of net load constraints is also added for each bus to reduce the net load fluctuation. The nonconvex model is then converted into a second-order cone programming formulation, which can be solved in an efficient manner. The proposed method is evaluated on a modified 33-bus flexible distribution network. The simulation results show that better flexibility can be achieved with slightly increased ESS investment costs. However, a large ESS capacity is needed to reduce the net load fluctuation to low levels, especially when the PV capacity is large. Full article

In the context of constructing new power systems, distribution networks are increasingly incorporating distributed resources such as distributed photovoltaic (PV) systems, decentralized wind turbines (WTs), and new types of energy storage system (ESS), which may lead to prominent issues such as voltage overruns and reverse heavy overloads in the distribution network. While distributed resources are valuable for voltage regulation, their regulation characteristics vary with their operation means, and the randomness and volatility of renewable power generation will also influence the optimization and regulation of voltage in the distribution network. This paper proposes a multi-timescale reactive power optimization and regulation method for distribution networks in a multi-source interactive environment. Firstly, the voltage regulation characteristics of distributed PV systems, decentralized ESSs, and distributed WTs are analyzed. Based on this analysis, a multi-timescale voltage optimization scheme for distribution networks using the MPC method is proposed, which optimizes the voltage regulation strategies for each distributed resource in a rolling manner. Furthermore, an event-triggered real-time voltage zoning control strategy based on voltage sensitivity is proposed to address the real-time sudden voltage overlimit problems. The modified IEEE 33-node system is used to verify the performance of the proposed method. Simulation results indicate that the issue of voltage overruns at distribution network nodes has been improved, and the intraday rolling optimization yields results are more realistic compared with the day-ahead optimization method. Full article

In view of the complexity and importance of nuclear research reactor (NRR) installations, it is imperative to uphold high standards of reliability and quality in the electricity being supplied to them. In this paper, the performance of low-voltage (LV) distribution feeders integrated with NRRs is improved in terms of reduced distribution loss, improved voltage profile, and reduced greenhouse gas (GHG) emissions by determining the optimal location and size of photovoltaic (PV) systems. In the second stage, the power quality of the feeder is optimized by reducing the total harmonic distortion (THD) by optimally allocating D-STATCOM units. In the third and fourth stages, the reliability and resilience aspects of the feeder are optimized using optimal network reconfiguration (ONR) and by integrating an energy storage system (ESS). To solve the non-linear complex optimization problems at all these stages, an efficient meta-heuristic Chernobyl disaster optimizer (CDO) is proposed. Simulations are performed on a modified IEEE 33-bus feeder considering the non-linear characteristics of NRRs, variability of the feeder loading profile, and PV variability. The study reveals that the proposed methodology can significantly improve the service requirements of NRRs for attaining sustainable research activities. Full article

High-penetration photovoltaic (PV) integration into a distribution network can cause serious voltage overruns. This study proposes a voltage hierarchical control method based on active and reactive power coordination to enhance the regional voltage autonomy of an active distribution network and improve the sustainability of new energy consumption. First, considering the reactive power margin and spatiotemporal characteristics of distributed photovoltaics, a reactive voltage modularity function is proposed to divide a distribution grid into voltage regions. Voltage region types and their weak points are then defined, and the voltage characteristics and governance needs of different regions are obtained through photovoltaic voltage regulation. Subsequently, a dual-layer optimal configuration model of energy storage that accounts for regional voltage regulation is established. The upper-layer model focuses on planned configurations to minimize the annual comprehensive operating cost of the energy storage system (ESS), while the lower-layer model focuses on optimal dispatch to achieve the best regional voltage quality. KKT conditions and the Big-M method are employed to convert the dual-layer model into a single-layer linear model for optimization and solution. Finally, an IEEE 33-node system with high-penetration photovoltaics is modeled using MATLAB (2022a). A comparative analysis of four scenarios shows that the comprehensive cost of an ESS decreased by 8.49%, total revenue increased by 19.36%, and the overall voltage deviation in the distribution network was reduced to 0.217%. Full article

This article proposes a modular system for prosumer installations composed of photovoltaic (PV) panels and energy storage (ES) integrated with the low voltage (LV) network through a common 4-wire AC/DC inverter. The novel idea is a control strategy for the inverter in which additional functionalities are incorporated. Apart from transmitting an active power generated by the PV source, the same inverter is used to manage energy generated by the PV and to compensate for the current unbalance, harmonics (including subharmonics and interharmonics) and reactive power of the prosumer loads. As a result of the algorithm operation, the currents flowing to the prosumer installation are sinusoidal, symmetrical and purely active, which results in voltage balancing and improving voltage waveforms at the point of common coupling (PCC). In this way, with the widespread use of this solution among prosumers, the impact of the prosumer installation on the distribution network is minimized, and power quality (PQ) disturbances such as unacceptable voltage rises, voltage unbalance and harmonics are avoided. The presented approach may be a solution to the problems network operators face nowadays due to the uncontrolled connection of PV sources. The proposed modular system is also beneficial for the prosumer as the instances of unacceptable overvoltage and, consequently, shutdowns of prosumer installations are reduced. The features of the proposed method are shown in relation to other means applied for PQ improvement in the networks with distributed generation. A principle of the control and the involving algorithm for the inverter is presented. The efficiency of the control strategy was tested in a simulation developed in the PSCAD/EMTDC program. The results of simulations are presented, and the proposed solution is concluded. Full article

The share of renewable energy resources in modern electrical power networks is increasing in order to meet environmental and technical targets. Consequently, energy researchers and power providers have been focusing on optimizing the integration of renewable energy into existing power grids. One of the most significant growing applications of renewable energy resources is residential photovoltaic (PV) systems; therefore, this paper discusses a new methodology to enhance the utilization of small-scale and medium-scale PV systems. For this purpose, this study proposes a user-friendly interface to help novice users optimally design their own PV projects with the highest possible utilization of the installed panels. Unlike the commercially available design tools, the proposed interface in this paper provides a higher degree-of-freedom computational process, as well as the option of improving the generated power quality, while maintaining the simplicity of the required tools and inputs. The proposed methodology mainly relies on a deep mathematical analysis considering different generation and consumption aspects, such as the load profile, time of usage, ambient temperature, PV system specifications and location. Furthermore, the mechanism of integrating a small portion of Energy Storage Systems (ESSs), to improve the quality of the extracted power, is also discussed in this study. The user interface provides the ability to estimate optimal ESS usage versus the estimated price when energy is urgently required. The case study was conducted in Riyadh, Saudi Arabia, and the results showed an essential improvement in the efficiency, solar fraction and power quality of the studied PV project, which can be extended to other home and distributed generation (DG) scales. Full article