A hybrid optimization approach to evaluating load capacity in distribution networks with new energy and energy storage integration

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.

As the penetration of distributed renewable energy increases, the phenomenon of bidirectional power flow in distribution networks becomes increasingly severe. Traditional regulation devices like OLTC (on-load tap changer) and CB (capacitor bank) cannot effectively mitigate reverse power flow in distribution networks due to their limitations. The transmission capacity of the distribution network under reverse power flow is approximately 50% of the rated capacity of the OLTC, leading to issues such as voltage limit violations and high wind and solar curtailment rates. This paper proposes a method for calculating the reverse power flow delivery capacity of distribution networks, quantitatively describing the distribution network’s delivery limits for reverse power flow. Based on this, a joint optimization model for multiple distribution networks with an SOP is established. The SOP is utilized to share reverse power flow delivery capacity among multiple distribution networks, enhancing operational economy and increasing the accommodation of the DG. Finally, the method’s effectiveness and correctness are verified in the IEEE 33-node system. The results validate that while joint operation does not enhance the reverse flow transmission capacity of a single distribution network, it can, through the shared reverse flow transmission capacity approach, elevate the reverse flow transmission capacity to approximately 70% during the majority of time periods.

With the large-scale access of distributed photovoltaic to the distribution network, the increase of node voltage has become the main reason for limiting the large-scale access of distributed PV, and effectively evaluating the maximum carrying capacity of distributed PV in the distribution network has become the research focus.In this paper, the voltage sensitivity-based method is used to analyze the distributed PV carrying capacity of the rural distribution network. Different scenarios are selected to maximize the distributed PV carrying capacity of the rural distribution network. Based on the voltage sensitivity analysis, the sensitive points of the rural distribution network after the distributed PV is connected are determined through the active sensitivity matrix. The maximum PV carrying capacity and acceptance rate of the rural distribution network are obtained without causing the voltage of the sensitive points to exceed the limit. Finally, a typical rural distribution network example is simulated by matlab simulation software. The simulation results show that this method can effectively evaluate the PV carrying capacity in rural areas.

In view of the problem that the existing distribution network energy storage location and capacity planning method is easy to cause the distribution network load risk coefficient to be too high, this paper carries out the research on the distribution network energy storage location and capacity planning method based on the improved gray wolf algorithm. The improved gray wolf algorithm is used to establish the planning objective function; the uncertain scenario of distribution network planning is considered, and the constraints of energy storage location and capacity planning are set. The scenario analysis is used to solve the location and capacity of energy storage. By comparing the proposed planning method with the heuristic-based planning method, it is concluded that the load risk coefficient is significantly reduced after the proposed planning method is used to complete the energy storage location and capacity planning of the distribution network, which is superior to the comparison method and promotes the safety and stability of distribution network operation.

This study proposes a TOPSIS‐based method for assessing the ability of distribution networks to accept electric vehicles. This method establishes an assessment index system in terms of the rationality, safety, and economy of the distribution network operation, and assesses the capacity of the distribution network in all aspects. Firstly, a fuzzy theory‐based model of users' charging psychology under the influence of time‐of‐use electricity price was constructed, and the spatio‐temporal distribution of EV charging loads in the target area was predicted using travel chain theory and Monte Carlo methods. Secondly, considering the rationality, safety and economy of the distribution network operation, a comprehensive evaluation index system for acceptability has been constructed. Then, a comprehensive weighting method for evaluation indexes based on AHP and entropy weight method is proposed, and the improved TOPSIS is used to evaluate the acceptance capacity of the distribution network when EV charging loads are connected in different ways. Finally, a typical IEEE33 distribution network is used to simulate the time and space distribution of the charging load, and taking the charging load access schemes proposed in this paper to verify the effectiveness of the evaluation method.

At present, the large-scale access to electric vehicles (EVs) is exerting considerable pressure on the distribution network. Hence, it is particularly important to analyze the capacity of the distribution network to accommodate EVs. To this end, we propose a method for analyzing the EV capacity of the distribution network by considering the composition of the conventional load. First, the analysis and pretreatment methods for the distribution network architecture and conventional load are proposed. Second, the charging behavior of an EV is simulated by combining the Monte Carlo method and the trip chain theory. After obtaining the temporal and spatial distribution of the EV charging load, the method of distribution according to the proportion of the same type of conventional load among the nodes is adopted to integrate the EV charging load with the conventional load of the distribution network. By adjusting the EV ownership, the EV capacity in the distribution network is analyzed and solved on the basis of the following indices: node voltage, branch current, and transformer capacity. Finally, by considering the 10-kV distribution network in some areas of an actual city as an example, we show that the proposed analysis method can obtain a more reasonable number of EVs to be accommodated in the distribution network.

The large-scale connection of the renewable energy with random and fluctuating output of the grid has a serious impact on the safe and stable operation of the grid. Considering the high cost of energy storage and the fluctuation of load, in this study, an optimization approach for designing the distribution network’s energy storage capacity is presented. This paper analyzes the uncertainty of new energy, and constructs a single distribution network energy storage station model based on the analysis results. In this paper, the typical daily total network loss is taken as the objective function for site selection and capacity planning. In this paper, particle swarm optimization algorithm is used to optimize the energy storage and capacity planning of distribution network. The experimental results show that this method can reduce the operating cost of distribution network and restrain the system load fluctuation. This method optimizes the economy and reliability of distribution network system.

A large number of distributed photovoltaic grids have exacerbated the problems of voltage limit, three-phase imbalance and power reversal of the low-voltage distribution network, bringing challenges to the stable operation of the distribution network. Aiming at the problem of absorbing a high proportion of distributed photovoltaics, this paper proposes a space-time coordination based on Voltage Source Converter (VSC) and energy storage by exploiting the power regulation potential of low-voltage AC and DC distribution networks and energy storage. Optimization. Firstly, the power transfer characteristics of VSC and energy storage at the spatial and temporal levels are analyzed; secondly, with the goal of minimizing PV cut-off and network loss, and energy storage and VSC power as optimization variables, a low-voltage AC-DC hybrid distribution is established. Finally, taking a typical low-voltage AC-DC hybrid distribution network as an example, the validity of the proposed method is proved by simulation, and the photovoltaic capacity of the low-voltage distribution network is improved.

After large-scale electric vehicles (EVs) are connected to the residential distribution network, community charging has become one of the main bottlenecks at present, especially in old residential areas. Therefore, the current residential distribution network’s ability to accept charging load and when and how the distribution network needs to be transformed have become meaningful research points. Based on the characteristics of the EVs’ charging load of residential areas on a typical day and the size of the target annual charging load, this paper analyzes the acceptance capacity of the charging load of the distribution network on typical weekdays and weekends. By taking the charging load characteristics, the charging time of EVs, the voltage of each node of the distribution network, the line capacity, the transformation capacity of the distribution station as constraints, and the maximum capacity of the residential distribution network to accept the charging load as the objective function, the charging load capacity of the residential distribution network is analyzed. The particle swarm optimization algorithm is employed to solve the optimized mathematical model. The simulation uses an actual residential distribution network as an analysis example, and the partition optimization results prove the correctness and feasibility of this proposed method.

In recent years, the integration of high proportions of clean energy into power systems has brought challenges to their reliability, and the large-scale commercial application of energy storage (ES) provides strong support. However, the market mechanism for energy storage is not mature enough, and the current pricing method for network charges is mainly used for system members with a single power flow direction such as power sources and loads, without addressing energy storage resources that can exchange power bidirectionally with the system. By responding to real-time electricity prices and load curves with charging and discharging strategies, an energy storage operation mode that maximizes the profits of the storage owner and is beneficial to the stability of the power network can be obtained. Based on the long-term incremental cost method, the economic benefits of energy storage in relieving network congestion and deferring investment are quantified to determine the network charges for energy storage resources. Based on the dynamic investment efficiency evaluation method, the returns of energy storage investors over the entire battery life cycle are analyzed and evaluated, and verified in the IEEE 33-node distribution system.

The article presents, a bi-level optimization framework for optimally deploying and managing solar and wind power base DG (Distribution Generator). An energy storage system (BESS) is also implemented in a power distribution network (PDN) to maximize the renewable hosting capacity of distribution networks. A new objective function is broached with the consideration of annual energy losses, reverse power flow into the grid, node voltage deviation, non-utilised BESS capacities and round-trip conversion losses of BESS. As a high installation and maintenance cost is associated with BESS it's observed to install one BESS in the system which is to be placed at the nodes of DG. Artificial intelligence based optimal control and management system is projected to adequately manage the high renewable power generation. Greedy strategy and self-adaptive crossover operator base monarch butterfly optimization (GCMBO) had been applied as an optimization tool. In order to ensure the efficacy of the presented model, it is tested and implemented on a 33-bus benchmark test distribution system under various test cases. Various simulation studies have been carried out which depicts the usefulness of the proposed methodology.

Thermoeconomic analysis of residential rooftop photovoltaic systems with integrated energy storage and resulting impacts on electrical distribution networks

With the large-scale access of distributed new energy into the distribution network, the evaluation on the accommodation capacity of distributed new energy in the distribution network has important guiding significance for the scientific planning of distributed generation and grid connection. Considering the parked and regionalized development trend of distributed power generation mode, this paper proposes a method for evaluating the distributed wind and photovoltaic power accommodation capacity of regional distribution networks based on time series production simulation from the perspective of practical applications. Based on the timing difference and complementarity of the load characteristics of substations on the distribution network side, we conducted an evaluation on the distributed wind and photovoltaic power accommodation capacity of the regional distribution network, and through taking the flexible operation mode of the distribution network side into consideration, realized the mutual assistance from time domain and space dimension of distributed new energy consumption and carrying capacity of various substations. Through establishing a mathematical model for the consumption and carrying capacity analysis in the multi-scenario modes such as local node consumption, local station consumption and allowing certain percentage of abandoned electricity, we came out the scale of the maximum consumption of distributed new energy in different scenarios as well as the overall grid connection layout scheme. In the end, we used such evaluation method to conduct an empirical analysis on the distribution network in certain region, verified the correctness and effectiveness of the model, and provided theoretical supports for guiding the regional distributed new energy overall planning and scientific grid connection.

This paper presents a planning approach for improving the rooftop photovoltaic (PV) hosting capacity and energy efficiency of distribution networks with the placement of voltage‐sourced converters (VSCs). The PV hosting capacity (PVHC) is the maximum amount of PV generation that a distribution network can accommodate without deteriorating the operational performance of the network. The PVHC is derived under the constraint of a maximum allowable network power loss. Two different types of VSCs are modelled for the placement in distribution networks to improve the PVHC. These are series and shunt VSCs. The series VSC is designed to inject a series voltage in quadrature with the line current to maintain a constant bus voltage. The shunt VSC is designed to improve the load power factor to unity by injecting a shunt compensating current in quadrature with the bus voltage. The objective function is to maximise the PVHC to obtain the maximum PV generation capacity to be integrated in each bus. The particle swarm optimisation is used as a solution approach. The results show that the placements of VSCs improve the PVHC. This also reduces energy loss when there is no PV generation available.

Assessments of the hosting capacity of electricity distribution networks are of paramount importance, as they facilitate the seamless integration of rooftop photovoltaic systems into the grid, accelerating the transition towards a more carbon neutral and sustainable system. This paper employs a deep reinforcement learning-based approach to evaluate the real-time hosting capacity of low voltage distribution networks in a model-free manner. The proposed approach only requires real-time customer voltage data and solar irradiation data to provide a fast and accurate estimate of real-time hosting capacity at each customer connection point. This study addresses the imperative for accurate electrical models, which are frequently unavailable, in evaluating the hosting capacity of electricity distribution networks. To meet this challenge, the proposed approach utilizes a deep neural network-based, data-driven model of a low-voltage electricity distribution network. This proposed methodology incorporates model-free elements, enhancing its adaptability and robustness. In addition, a comparative analysis between model-based and model-free hosting capacity assessment methods is presented, highlighting their respective strengths and weaknesses. The utilization of the proposed hosting capacity estimation model enables distribution network service providers to make well-informed decisions regarding grid planning, leading to cost minimization.