Minimum Network Loss Research Articles

Research on multi-time scale Volt/VAR optimization in active distribution networks based on NSDBO and MPC approach

To explore the potential of all-round and multiangle voltage and reactive power control to reduce losses in a new power system with a high proportion of distributed resources connected to a distribution network, this study proposes a Volt/VAR optimization method for distribution networks using a nondominated sorting dung beetle optimizer (NSDBO) and model predictive control (MPC). According to the characteristics of the various adjustable resources in the network, they are divided into day-ahead and intraday optimizations. The optimization process in the day-ahead stage uses the NSDBO to create a discrete equipment scheduling plan. The optimization process in the intraday stage combines the discrete equipment scheduling plan with the MPC to create the scheduling plan for photovoltaics, energy storage, and vehicle charging stations. Two-stage optimization scheduling is achieved with the lowest discrete equipment regulation cost and minimization of network loss and node voltage deviation penalty cost as the optimization goal. The feasibility of this method for coordinating various resources in the network, processing discrete and continuous variables, and coping with the volatility and uncertainty of high-proportion distributed resources is verified through case analysis. The effectiveness of this method in improving the security and economy of distribution networks is demonstrated. The superiority of the solution speed and quality is also confirmed.

Collaborative operation of active distribution network considering PV-storage-charging-compensation

Abstract With the conjunction of clean energy generation and electric cars, the power distribution network is gradually shifting from a traditional one-way radiative network to an active network. Coordinated control of these controllable elements of “source-grid-load-storage” can improve the operating voltage level and economic benefits, and reduce the load peak and off-peak difference. A collaborative dispatch model for active power distribution networks including PV-storage-charging-compensation is constructed in this paper, with the goal of minimizing network losses and PV curtailment costs. The cone transformation is used to handle non-linear constraints of power flow. Compared with existing ordered charging control methods, the constructed model can maximize the control efficiency of the distribution grid, enhance the PV absorption capacity, and effectively slow down the upgrading and renovation of the distribution network.

Real-Time Power Regulation of Flexible User-Side Resources in Distribution Networks via Dual Ascent Method

Flexible user-side resources are of great potential in providing power regulation so as to effectively address the challenges of reverse power flow and overvoltage issues in distribution networks characterized by high photovoltaic (PV) penetration. However, existing distributed algorithms typically implement control signals after the convergence of the algorithms, making it difficult to track frequent and rapid fluctuations in PV power outputs in real time. Given this background, an online-distributed control algorithm for the real-time power regulation of flexible user-side resources is proposed in this paper. The objective of the established control model is to minimize network losses by dynamically adjusting active power outputs of flexible user-side resources and reactive power outputs of PV inverters while respecting branch power flow and voltage magnitude constraints. Furthermore, by deconstructing the centralized problem into a primal–dual one, a distributed control strategy based on the dual ascent method is implemented. With the proposed method, agents can achieve global optimality by exchanging limited information with their neighbors. The simulation results verify the good balance between economic efficiency and voltage control performance of the proposed method.

Collaborative Optimization for Active Distribution Network with Soft Open Point and Energy Storage

Background: The increasing use of high-penetration distributed clean energy has led to voltage fluctuations in the distribution network that surpass safety limits and pose challenges to economic and low-carbon operation. Traditional voltage regulation devices are unable to meet the real-time high-precision voltage control needs when encountering frequent fluctuations due to physical limitations. The Soft Open Point (SOP) can provide continuous reactive power to achieve rapid voltage regulation. Objective: We propose a collaborative optimization approach that fully considers the regulatory capabilities of SOP and energy storage to eliminate voltage violations and reduce network losses. Methods: This study introduces an active distribution network reactive power voltage optimization approach that incorporates intelligent SOP and energy storage, combining conventional devices (such as on-load tap changers and capacitor banks) with contemporary devices (such as SOP, distributed generators, and energy storage) to establish synergy. A coordinated optimization model was developed, considering various operational constraints to minimize network losses and regulate the voltage of distribution network nodes. By using linearization and quadratic relaxation, the original non-convex mixed-integer non-linear optimization model was transformed into a Mixed-Integer Second-Order Cone Programming (MISOCP) model to meet the requirements of rapid voltage regulation. Results: A case study was conducted on the IEEE 33-node test system, showing that the proposed approach effectively reduces network losses and eliminates voltage violations. Conclusion: The feasibility and effectiveness of the proposed methodology have been validated, confirming its ability to ensure the safe and economic operation of active distribution networks.

Optimal Scheduling Strategy for Urban Distribution Grid Resilience Enhancement Considering Renewable-to-Ammonia Coordination

The integration of numerous distributed energy sources into the power system offers exciting opportunities to enhance the resilience of distribution networks. It is worth noting that the renewable-to-ammonia system has the potential to alleviate the multi-temporal and spatial imbalance of the power system. Therefore, this paper proposes a mathematical model for a renewable-to-ammonia system, taking into account the material balance and power balance of each unit. Based on this, this paper further explores the optimization scheduling method for flexible ammonia loads in distribution networks. A relaxation method for branch flow models in distribution networks based on second-order cone programming is proposed. An optimization scheduling model for flexible ammonia loads in distribution networks is constructed to minimize network loss. Moreover, considering the environmental advantages of the renewable-to-ammonia system, this paper compares the changes in hydrogen production technologies under different carbon emission constraints. Finally, a case study of the IEEE 33-node system is adopted to verify the effectiveness of the proposed model and method. It indicates that the renewable-to-ammonia system has environmental benefits and can reduce network loss to a certain extent.

Evaluation Method for Voltage Regulation Range of Medium-Voltage Substations Based on OLTC Pre-Dispatch

A new energy industry represented by photovoltaic and wind power has been developing rapidly in recent years, and its randomness and volatility will impact the stable operation of the power system. At present, it is proposed to enrich the regulation of the power grid by tapping the regulation potential of load-side resources. This paper evaluates the overall voltage regulation capability of substations under the premise of considering the impact on network voltage security and providing a theoretical basis for the participation of load-side resources of distribution networks in the regulation of the power grid. This paper proposes a Zbus linear power flow model based on Fixed-Point Power Iteration (FFPI) to enhance power flow analysis efficiency and resolve voltage sensitivity expression. Establishing the linear relationship between the voltages of PQ nodes, the voltage of the reference node, and the load power, this paper clarifies the impact of reactive power compensation devices and OLTC (on-load tap changer) tap changes on the voltages of various nodes along the feeder. It provides theoretical support for evaluating the voltage regulation range for substations. The day-ahead focus is on minimizing network losses by pre-optimizing OLTC tap positions, calculating the substation voltage regulation boundaries within the day, and simultaneously optimizing the total reactive power compensation across the entire network. By analyzing the calculated examples, it was found that a pre-scheduled OLTC (on-load tap changer) can effectively reduce network losses in the distribution grid. Compared with traditional methods, the voltage regulation range assessment method proposed in this paper can optimize the adjustment of reactive power compensation devices while ensuring the voltage safety of all nodes in the network.

Optimal Scheduling of the Active Distribution Network with Microgrids Considering Multi-Timescale Source-Load Forecasting

Integrating distributed generations (DGs) into distribution networks poses a challenge for active distribution networks (ADNs) when managing distributed resources for optimal scheduling. To address this issue, this paper proposes a day-ahead and intra-day scheduling approach based on a multi-microgrid system. It starts with a CNN-LSTM-based generation and load forecasting model to address the impact of generation and load uncertainties on the power grid scheduling. Then, an optimal day-ahead and intra-day scheduling framework for ADN and microgrids is introduced using predicted generation and load information. The day-ahead scheduling is responsible for optimizing the power interactions between ADN and the connected microgrids, while intra-day scheduling focuses on minimizing the operational costs of microgrids. The effectiveness of the proposed scheduling strategy is verified via case studies performed on a modified IEEE 33-node ADN. The results show that the network loss of ADN and the operation costs of microgrids are reduced by 17.31% and 32.81% after the microgrid is integrated into the ADN. The peak-valley difference in microgrids decreased by 13.12%. The simulation shows a significant reduction in operational costs and load fluctuations after implementing the proposed day-ahead and intra-day scheduling strategy. The seamless coordination between the day-ahead scheduling and intra-day scheduling allows for the precise adjustment of transfer power, alleviating peak load demand and minimizing network losses in the ADN system.

A new data-driven distributed power grid load storage resource-wide area voltage self-optimization control method for distribution system

Abstract Different from the conventional energy distribution network, which solely relies on energy distribution, the novel distribution system gradually exhibits a complex and interconnected form of various resources, such as source network load storage. These resources possess characteristics of “multi-point, wide area, and small amount” distribution. Investigating the potential for wide-area voltage regulation by using distributed resources like renewable energy power generation, distributed energy storage, and flexible loads is crucial for constructing a highly integrated source network load storage in the new type of distribution system. This paper proposes a novel data-driven method for optimizing control of wide-area voltage through distributed power network load storage resources. By comprehensively and cooperatively controlling renewable energy power generation, energy storage systems, and flexible loads, this approach enhances the quality of wide-area voltage in the distribution network while ensuring optimal utilization of energy storage capacity and minimizing network losses. The effectiveness and advanced nature of this method are demonstrated by applying it to an urban distribution network example. Compared to traditional voltage regulation methods, this approach aligns better with the requirements for voltage regulation in the new type of distribution network.

Optimal configuration method of photovoltaic energy storage in distribution network based on source-load coordination

Abstract To enhance the configurability of photovoltaic energy storage within distribution network systems and foster synchronized development of power sources and loads, a source-load coordinated approach for optimal photovoltaic energy storage configuration in distribution networks is introduced. An alternative multi-objective framework for optimal allocation of photovoltaic energy storage capacity in distribution networks is formulated, which is the optimal goal of maximum economic benefit of photovoltaic energy storage, the optimal goal of minimum network loss and the optimal goal of source-network load coordination. The constraints of the system’s power flow, energy storage charging and discharging capabilities, and an optimized allocation strategy for energy storage are established, and the objective function is solved with full consideration of source-network load coordination factors. The energy storage optimization is updated in the iterative process. Based on the update results, the process for optimal allocation of photovoltaic energy storage in the distribution network has been devised to attain the most efficient allocation. Experimental results indicate a minimal discrepancy between the actual and specified energy storage output, along with a reduced average output power resulting from the optimized photovoltaic energy storage configuration, which shows excellent performance in energy storage optimization configuration.

Analysis of power quality and additional loss in distribution network with distributed generation

As the penetration of distributed generation in the power grid, the power quality issues in the active distribution network are becoming increasingly diversified, hence followed with complex changes of the additional loss. The article investigates the additional loss caused by disorder power flow and power quality disturbances in the active distribution network. Firstly, the active distribution network model is established based on typical parameters. The impact of the access of distributed generation on power quality issues, including three-phase imbalance, harmonic distortion and voltage deviation and rise, is analyzed. Then, the influence of different power quality disturbances on additional loss with distributed generation is quantitatively explored. By introducing the concept of harmonic three-phase imbalance, the article analyzes the variation characteristics of network loss under the disturbances of composite power quality issues, and introduces the concept of a loss reduction interval, whose size is related to the average current. Finally, the validity of the analysis is verified through simulation. This article provides a new approach for loss analysis in distribution network containing distributed generation, which is beneficial for identifying and minimizing network loss in distribution networks.

Research on the minimum network loss optimization control strategy of the active distribution network based on the distributed power flow controller

With large-scale access to distributed new energy, the power flow form of the distribution network tends to be complex. The distribution loop network rate increases, resulting in serious circulation of the distribution network, which places significant pressure on the loss of the distribution network. The problem of network loss change caused by distributed new energy grid connection, coupled with the inconsistency between load fluctuation and output power change of distributed new energy, will have a significant impact on the safety, reliability, and economic operation of the distribution network. Therefore, it is urgent to have good economic and technical means of power flow control. This paper proposes a minimum network loss optimization control strategy for active distribution networks based on a distributed power flow controller. First, according to the characteristics of the distribution network load and the new energy power supply along the distribution line, the multiple sub-modules of the distributed power flow controller are arranged in sections in the distribution loop network line. The regulation effect of the distributed power flow controller on the voltage and power flow of the distribution loop network is analyzed. The loop network current equation and power loss equation after the distributed power flow controller are constructed, and the power equation with the minimum active loss of the active distribution loop network is derived. Second, the control strategy for minimum network loss of the active distribution network based on the distributed power flow controller is proposed. Finally, the simulation model of the active distribution loop network is constructed by PSCAD/EMTDC. The loss of the loop network before and after the distributed power flow controller is simulated and analyzed, and the effectiveness of the proposed strategy is verified.

Research on a Three-Stage Dynamic Reactive Power Optimization Decoupling Strategy for Active Distribution Networks with Carbon Emissions

The reactive power optimization of an active distribution network can effectively deal with the problem of voltage overflows at some nodes caused by the integration of a high proportion of distributed sources into the distribution network. Aiming to address the limitations in previous studies of dynamic reactive power optimization using the cluster partitioning method, a three-stage dynamic reactive power optimization decoupling strategy for active distribution networks considering carbon emissions is proposed in this paper. First, a carbon emission index is proposed based on the carbon emission intensity, and a dynamic reactive power optimization mathematical model of an active distribution network is established with the minimum active power network loss, voltage deviation, and carbon emissions as the satisfaction objective functions. Second, in order to satisfy the requirement for the all-day motion times of discrete devices, a three-stage dynamic reactive power optimization decoupling strategy based on the partitioning around a medoids clustering algorithm is proposed. Finally, taking the improved IEEE33 and PG&E69-node distribution network systems as examples, the proposed linear decreasing mutation particle swarm optimization algorithm was used to solve the mathematical model. The results show that all the indicators of the proposed strategy and algorithm throughout the day are lower than those of other methods, which verifies the effectiveness of the proposed strategy and algorithm.

Multi-objective optimization and scheduling research on electric bicycle charging and discharging in distribution networks

Abstract With the continuous development of urbanization, the rapid growth of electric bicycles has become a prominent trend. The number of battery-swapping stations for convenient battery replacement has also increased. The large-scale and unregulated connection of electric bicycles and battery-swapping stations to the power grid can significantly impact the distribution network, leading to localized overloads and affecting grid stability. Therefore, this paper proposes a multi-objective optimization scheduling strategy for electric bicycle charging and dis-charging based on both the transmission and distribution systems. Firstly, in the transmission system, the objective is to reduce the operating costs of generators and user charging costs. Sub-sequently, in the distribution system, the goal is to minimize network losses, considering system constraints and the spatial migration characteristics of electric bicycles. The study establishes a multi-objective optimization problem. Simulation analyses were conducted on the power system models based on a standard 10-machine transmission network and an IEEE 33-node distribution network to verify the effectiveness of the proposed multi-objective optimization scheduling strategy.

Reactive Power Optimization in Distribution Networks of New Power Systems Based on Multi-Objective Particle Swarm Optimization

The new power system effectively integrates a large number of distributed renewable energy sources, such as solar photovoltaic, wind energy, small hydropower, and biomass energy. This significantly reduces the reliance on fossil fuels and enhances the sustainability and environmental friendliness of energy supply. Compared to distribution networks in traditional power systems, the new-generation distribution network offers notable advantages in improving energy efficiency, reliability, environmental protection, and system flexibility, but it also faces a series of new challenges. These challenges include potential harmonic issues introduced by the widespread use of power electronic devices (such as inverters for renewable energy generation systems and electric vehicle charging stations) and the voltage fluctuations and flickering caused by the intermittency and uncertainty of renewable energy generation, which may affect the normal operation of electrical equipment. To address these challenges, this study proposes an optimization model aimed at minimizing network losses and voltage deviations, utilizing traditional capacitor adjustments and static var compensators (SVCs) as optimization measures. Furthermore, this study introduces an improved version of the multi-objective particle swarm optimization (MOPSO) algorithm, specifically enhanced to address the unique challenges of reactive power optimization in modern distribution networks. The test results show that this algorithm can effectively generate a large number of Pareto optimal solutions. The application of this algorithm on a 33-node network case study demonstrates its advantages in reactive power optimization. The optimization results highlight the effectiveness and feasibility of the proposed improved algorithm in the application of distribution network reactive power optimization, offering users a uniform and diverse range of reactive compensation solutions.

Coordinated optimization of source‐grid‐load‐storage for wind power grid‐connected and mobile energy storage characteristics of electric vehicles

AbstractThe rapid growth in the number of electric vehicles (EVs), driven by the ‘double‐carbon’ target, and the impact of uncontrolled charging and discharging behaviour and discharged battery losses severely limit electric vehicles’ low carbon characteristics. Existing research on systemic low‐carbon emissions and electric vehicle charging and discharging issues is usually determined by considering only carbon trading markets or charging and discharging management on the source side. In this regard, a coordinated and optimized operation model that considers the participation of electric vehicle clusters in deep peaking and the source network load and storage adjustable resources is proposed. The upper layer establishes a real‐time price‐based demand response mechanism for the load side with the minimum net load fluctuation as the objective function; the middle layer establishes a comprehensive operation mechanism for the source and storage side that includes an orderly charging and discharging peaking compensation mechanism for electric vehicles, and a deep peaking mechanism that takes into account clean emission, and constructs an optimal operation model with the minimum comprehensive operating cost as the objective function; the lower layer establishes a distribution network loss minimization model for the network side that takes into account the orderly charging and discharging of electric vehicle as the objective function. The optimal load model with the objective function of minimizing the distribution network loss is established at the lower level. Finally, the original problem is transformed into a mixed integer linear programming problem, and the model's effectiveness is verified by setting different scenarios. The model reduces the total cost by 22.22%, improves the wind power consumption rate by 19.55%, reduces the actual carbon emission by 16.66%, and reduces the distribution network loss by 13.91% compared to the basic model.

Assessment of New Energy Consumption Capacity of Grid Based on Edge Computing

Distributed photovoltaic grid integration brings many negative effects to the distribution network, and carrying out the research on its consumption capacity (CC) in the distribution network is the key to guarantee the safe and stable operation of the distribution network. This paper takes the maximum power access mode and minimum network loss as the optimization objectives, and takes voltage deviation, voltage fluctuation and short circuit capacity as the constraints to construct the evaluation model of distributed PV power generation CC. In view of the above problems, this paper uses edge computing to construct the evaluation of new energy (NE) CC of the grid, and applies it to the IEEE33 node network and the actual network. Finally, the established mathematical model and algorithm are validated (relative to Monte Carlo, the evaluation time of edge computing and time series production simulation is shorter, 13.11s and 23s, respectively).

Wind power access site selection method based on scenario probability

The fluctuation of new energy output makes the power system operation more complex, and the traditional power system tide calculation is no longer fully applicable. Therefore, the determination of wind farm output and site selection is important and fundamental research at present. Based on this, a combination of wind farm output scenario and probability is proposed. The site selection model with the minimum network loss of the distribution network as the optimization objective is proposed. First, the wind farm output is clustered into several typical scenarios by K-Means, and the output time series model and the probability of each typical scenario are obtained. The probabilities of each scenario are multiplied by the network losses under that scenario. Then, the network losses obtained for all individual scenarios are summed up and used as the minimum objective function. The model is solved by a genetic algorithm with tidal current constraint and node voltage constraint to derive the optimal wind farm access point. With the IEEE33 node, simulations are performed to derive the minimum value of the daily average network loss, which verifies the rationality and feasibility of the optimized model.

Optimal Scheduling of New Energy and Thermal Power Combined Operation System

Nowadays, the power grid with a high proportion of renewable energy is increasingly popular. Taking a new energy power system including wind power (WP), photovoltaic power (PP), and pumped storage power (PSP) as an example, in order to solve the problem of resource waste such as massive WP-PP abandonment and power losses caused by the randomness and volatility of new energy, this paper establishes a combined power generation model of WP-PP-PSP-thermal power (TP). It achieves the goal of minimum network loss and minimum WP-PP abandonment in the combined power generation system according to the characteristics of PSP and considering various constraints and distribution network reconfiguration. Then, the second-order cone relaxation (SOCR) technology is used to transform the model into a mixed second-order cone programming model. Finally, the design scheme is tested by the improved IEEE 33-bus system, and the effectiveness of the algorithm is analyzed. The results show that peak cutting and valley filling can be realized when PSP energy is added to the WP-PP grid connection system.

An advanced multi-objective collaborative scheduling strategy for large scale EV charging and discharging connected to the predictable wind power grid

With the large-scale EV connected into the wind power grid, the intermittent, fluctuating and stability bring rigorous challenges for power quality and dispatch, thus wind curtailment becomes more serious. This paper proposes an advanced multi-objective collaborative scheduling strategy to improve the accuracy of the predictable wind grid and ensure the stable operation of the grid by optimizing wind power and EV synergistically. Firstly, a hybrid power prediction method is proposed based on density-based spatial clustering applied to noise (DBSCAN) and partial least squares regression (PLSR) combined with long and short-term memory neural network (LSTM). It establishes a linear component prediction mode to predict the nonlinear wind power efficiently. Secondly, a multi-objective optimization scheduling model is established according to the predicted wind power, considering the vehicle-to-grid (V2G) characteristics of EV, grid load fluctuations, wind curtailment capacity, EV charging costs, and grid losses. The global control aims to achieve overall energy balance and optimize the charging and discharging of EV in a time dimension under constrained conditions. Additionally, based on the total number of EV charging and discharging determined by the global level control, a spatial scheduling optimization is conducted for EV and wind power by the distribution network control, with the goal of minimizing network losses in the distribution network system. Finally, through simulation and comparison of four scenarios, the results demonstrate that the proposed method achieves synergistic optimization of wind power and EV. This paper provides an efficient solution for the optimized scheduling of large-scale EV connected to the predictable wind power grid.

Research on control strategy of distributed photovoltaic cluster based on improved particle swarm-gray wolf coupling algorithm

Conducting research on cluster control strategies for distributed photovoltaic systems to address voltage fluctuations and reverse power flow caused by large-scale distributed photovoltaic integration is crucial foundational work in establishing a new power system and ensuring its safe and stable operation. Based on the division of distributed photovoltaic cluster, this paper takes distributed photovoltaic cluster as the intermediate layer of control, and researches the two-layer control strategy of inter-cluster coordination and intra-cluster autonomy. The inter-cluster coordination strategy is located at the upper layer, and this strategy based on the power grid structure of the controlled area, comprehensively considering the observability, controllability, degree and betweenness centrality of each node, the latter two characterize the spatial location of the nodes. The dominant nodes of each cluster are selected by the above multiple indicators. The multi-objective inter-cluster coordination control model respectively focusing on the minimum voltage deviation of the dominant node or the minimum network loss of the system is constructed according to whether the voltage of the dominant node crosses the limit or not, and the improved particle swarm-gray wolf coupling algorithm (PSO-GWO) is used to generate the output indicators of each cluster. The intra-cluster autonomy strategy is at the lower layer. In the shortage of adjustable PV resources connected to the dominant node, the intra-cluster autonomy strategy adopting the control sequence of dominant node-downstream node-upstream node to distribute the output indicators of inter-cluster coordination within the cluster; In addition, in view of the situation that the cluster cannot receive the output indicators due to communication failure, The intra-cluster autonomy control model is constructed with the goal of minimum voltage deviation and minimum network loss of the dominant node in the cluster. The distributed photovoltaic inter-cluster coordination + intra-cluster autonomy two-layer regulation strategy is used to simulate the improved IEEE33-nodes system under multiple scenarios. The results show that the proposed control strategy can effectively solve the voltage overlimit, power flow back and other problems, and play an important role in ensuring the safe and economic operation of the system.