Load recovery strategy based on mobile energy storage flexibility and distribution network reconfiguration

Compared with traditional stationary energy storage system (SESS), mobile energy storage system (MESS) has power transfer ability in both spatial and temporal dimensions. Thus, it can provide greater flexibility in power system auxiliary services. To simplify and clarify the optimal scheduling problem, a novel “virtual switch” indicator is defined to describe the scheduling of MESS and an equivalent reconfiguration method is proposed by analogy with distribution network reconfiguration. Based on the method proposed, the dynamic economic scheduling of MESS in distribution networks(DNs) can be formulated as a scenario-based stochastic optimization problem, where the uncertainties of traffic flow and renewable energy output are considered by generating scenarios using Monte Carlo simulation. The objective function is to maximize the profit of distribution network operator. The time and cost of MESS scheduling are transformed into those of virtual switch switching in distribution network reconfiguration. Therefore, the operation scheduling problem of a MESS is equivalent to a distribution network reconfiguration problem with a SESS and several virtual switches. In this way, a grid-traffic network coupling problem is simplified to a pure distribution network problem, which provides a novel way to solve the MESS scheduling problems. Finally, simulation results on the IEEE 33-bus distribution system and simple traffic network verify the validity and feasibility of the method proposed.

In recent years, the power outages caused by catastrophic weather events have become an imperative issue in power system research. Mutual impacts of pre-and post-event operation, uncertainties during system recovery, as well as binary decision variables are still challenging. To address these issues, this paper proposes an adaptive robust load restoration method for active distribution networks which coordinates network reconfiguration, mobile energy storage systems (MESSs) and repair crews (RCs). In the pre-event stage, pre-positioning of MESSs and proactive network reconfiguration are conducted to enhance system survivability. In the post-event stage, dynamic network reconfiguration is coordinated with scheduling MESSs and RCs, to restore all loads under uncertainties. A mixed integer second-order conic programming model which also contains practical voltage dependent loads is formulated. Further, a two-stage robust optimization method is applied to guarantee solution robustness against multiple uncertainties including fault locations. To optimize the binary recourse variables in the post-event stage, a feasibility pump based solution algorithm is developed. Numerical simulations conducted on a 33-bus system demonstrate effectiveness of the proposed load restoration method, high efficiency of the solution algorithm and high solution robustness.

This paper presents optimal reconfiguration of radial distribution network with integration of distributed energy resources (DER) using improved particle swarm optimization (PSO) algorithm. The benefits of DER integration in distribution system are reducing power losses, improving voltage profiles and load factors, eliminating system upgrades, and reducing environmental impacts. However, the presence of DER could also cause technical problems in voltage quality and system protection. Reconfiguration of distribution network is aimed to minimize power loss and to improve voltage quality in order to enhance the distribution system performance. In this study, reconfiguration method is based on an improved PSO. The method has been tested in a 60-bus Bantul distribution network of Yogyakarta Special Region province, Indonesia. The simulation results show the importance of reconfiguring the network for enhancing the distribution system performance in the presence of DER.

This paper studies the reciprocal impact of distribution network protection and reconfiguration of active Nowadays, with increasing energy consumption in distribution networks and the demand of consumers to buy highly reliable power, it is of high importance to establish adapt between protection and operation of the power system. distribution system. Accordingly, a distribution network reconfiguration is carried out to find the optimal switching operations in an economic way. Since the switching operations will change network topology as well as short circuit level of buses, the protection coordination may be invalid. To address this issue, constraints of the coordination of protection relays and fuses is being formulated and added to the reconfiguration problem. Moreover, the nonlinear equations of the problem are linearized and are transformed the reconfiguration problem into Mixed-Integer Linear Programming (MILP) one to achieve the global optimal solution. The proposed method has been implemented on 33 bus distribution network. The results clearly show effectiveness of the active and reactive power management in an intelligent distribution network considering protection concepts.

Electricity distribution networks in densely populated urban areas are, in order to increase the reliability of supply, usually designed as meshed networks, though they always operate in radial configuration. Reconfiguration of distribution networks is achieved through switching operations on switching devices of distribution network branches (switching on/off of a branch). It is often the case that the existing configuration is not the configuration with minimum power losses. With the new computer program, developed by the authors, it is possible to find the network configuration that enables operation with minimum losses. The program proposes the best network configuration to the dispatcher, and the latter makes his own decision whether to change the network configuration or not. The mathematical algorithm for the network reconfiguration is presented in the paper, as well as results of the reconfiguration of the electricity distribution network of the city of Maribor, Slovenia.

Optimal distribution network reconfiguration (DNR), distributed generations location and sizing (DGs-LS), tap changer adjustment (TCA), and capacitors bank location and sizing (CAs-SL) are different methodologies used to reduce loss and enhance the voltage profile of distribution systems. DNR is the process of changing the network topography by changing both sectionalized and tie switch states. The optimal location looks to find the optimal setting of the DG and CA within the distribution network. Optimal size seeks to find the optimal output generation of both DG and CA. The TCA looks to find the optimal position for TC. These methods are challenging optimization problems and resort to meta-heuristic techniques to find a globally optimal solution. This paper presents a new methodology with which to simultaneously solve the problem of DNR, DGs-LS, TCA, and CAs-SL in distribution networks. This work aims to minimize active and reactive power losses, including voltage profile improvement using a multi-objective decision approach. The firefly algorithm (FA) and analytic hierarchy process (AHP) are used to optimize the fitness function and determine the function weight factors through the use of MATLAB software. Several scenarios were considered on the IEEE 69-bus network. In terms of active power and reactive losses, reductions in the test system of 96.16% and 92.7%, respectively, were achieved, evidencing the positive impact of the proposed methodology on distribution networks.

We consider a continuous multi-facility location-allocation problem that aims to minimize the sum of weighted farthest Euclidean distances between (closed convex) polygonal and/or circular demand regions, and facilities they are assigned to. We show that the single facility version of the problem has a straightforward second-order cone programming formulation and can therefore be efficiently solved to optimality. To solve large size instances, we adapt a multi-dimensional direct search descent algorithm to our problem which is not guaranteed to find the optimal solution. In a special case with circular and rectangular demand regions, this algorithm, if converges, finds the optimal solution. We also apply a simple subgradient method to the problem. Furthermore, we review the algorithms proposed for the problem in the literature and compare all these algorithms in terms of both solution quality and time. Finally, we consider the multi-facility version of the problem and model it as a mixed integer second-order cone programming problem. As this formulation is weak, we use the alternate location-allocation heuristic to solve large size instances.

Mobile energy storage has the advantages of high mobility, environmental friendliness, and wide application scenarios. It is widely used in important load protection, outdoor emergency power supply, and distribution network capacity expansion scenarios. This paper proposes a multi-objective configuration method for flexibly supporting the optimal operation of regional distribution networks. It aims to address the configuration problem of multi-mobile energy storage supporting regional power grid operation. A multi-objective optimization operation model for renewable energy distribution networks has been established. A multi-objective optimization method combining the normalized normal constraint method and improved composite method is proposed. It can achieve optimization decision-making of mobile energy storage positions, charging and discharging states, and power at different periods. Finally, simulation analysis is conducted on the IEEE 33 node distribution system to verify the effectiveness of the control method and algorithm.

Efficient energy storage technology is one of the key elements to enhance the flexibility, economy, and security of the power system. With the continuous development of energy storage technology, containerized mobile energy storage is coming into view, which has offered promising opportunities to improve distribution network (DN) performances and grid operating factors against emergencies. This paper proposes the concept of mobile compressed air energy storage (CAES) for an electric DN. The movable air storage tanks with stored energy are transported by trucks and placed at some distribution nodes/buses to improve DN performance. To overcome routing challenges for trucks, the configuration of the grid is mapped on the urban region of the city of Sydney, Australia, using Google map’s Application Programming Interface. This approach can accurately model distances between current and targeted locations, unavailability of tanks during traveling, route congestions, and fuel consumptions. A new heuristic mathematical method is proposed in this paper to convert the constraints of the mobile CAES (MCAES) model into feasible search spaces, which significantly improves the convergence quality and speed. Operating results for both stationary and mobile CAESs are presented and compared. The methodology is applied to IEEE 136-bus DN in addition to IEEE 33-bus DN to demonstrate the competence of MCAES for larger-scale grids in optimizing the total operating profit, active power loss, energy not supplied, and voltage stability index of the grid. The crucial application of the proposed model can deal with natural disasters to avoid large-scale power outages and, eventually, mitigate the power system damages.

This study proposes a distributed load restoration method to effectively model and solve the load restoration problem of integrated power distribution and natural gas systems (IDGSs) after faults for resilient operation. The IDGS load restoration problem is devised as a mixed-integer second-order cone programming (MISOCP) model to minimize the total load loss cost of IDGS. This model considers distribution network reconfiguration and the coupling effect between the power distribution system (PDS) and natural gas system (NGS). Moreover, a bidirectional gas flow model is used to offer more flexibility for load restoration when the NGS is faulty. By considering the independent management and information privacy of each energy subsystem, the proposed model is solved using an alternating direction method of multipliers (ADMM) algorithm in a distributed manner. Furthermore, an adaptive-ADMM algorithm is used for convergence acceleration. Finally, numerical studies are conducted to validate the proposed methods effectiveness.

The penetration of distributed generation (DG) units has been steadily increasing in distribution networks (DNs). However, oversizing and improper locating or operating of them can increase the losses or deteriorate the voltage profile. In this regard, distribution network reconfiguration (DNR) can be envisioned as a solution to maximize the DG penetration while improving the voltage profile and minimizing the losses. So, a study on DNR with the presence of DGs is necessary, in which the switching action costs are taken into consideration, since they can impose an extra cost on the daily operations. This study proposes a solution to solve the siting, sizing, and operating of DGs and DNR problems simultaneously considering switching action costs, losses costs, and reactive power generation of DGs. To consider the reactive power limits, their capability curve (P–Q curve) is included in the model. DNR is a combinatorial optimization problem, while the entire problem is modeled as a mixed-integer nonlinear programming problem. To solve that, tabu search algorithm (TSA) as one of the most efficient global solver for combinatorial problems is used, and its results are validated by particle swarm optimization (PSO) algorithm. To enlighten the effectiveness of the proposed approach, 5 scenarios are defined on IEEE 33-bus and IEEE 69-bus test systems. The results are also compared to previous studies. It is shown that, thanks to embedding the reactive power generation and switching costs at the same time, maximum loss reductions can be realized, while the results are more realistic and reliable.

Distribution network reconfiguration (DNR) is a technique that changes the status of sectionalizing and tie switches for various purposes such as loss minimization, voltage profile improvement, load leveling, and hosting capacity increase. Although previous algorithms for DNR show good performance, they still have practical limitations. Most of the algorithms assumed that a central coordinator knows all parameters and/or perfect states in a distribution network. Reinforcement learning which is a model-free optimization technique can be a key way to overcome these limitations. This work proposes a DNR scheme using deep reinforcement learning to minimize power loss defined by the amount of line loss and renewable energy curtailment. We model the DNR problem as a Markov decision process (MDP) problem and apply the reinforcement learning algorithm to solve this problem in real-time. Simulation result using 33-bus radial distribution system shows that the proposed scheme shows similar performance compared to an existing method which uses all information on the distribution network.

The purpose of distribution network reconfiguration (DNR) is to determine the optimal topology of an electricity distribution network, which is an efficient measure to reduce network power losses. Electricity load demand and photovoltaic (PV) output are uncertain and vary with time of day, and will affect the optimal network topology. Single-hour deterministic DNR is incapable of handling this uncertainty and variability. Therefore, this paper proposes to solve a multi-hour stochastic DNR (SDNR). Existing solution methods for DNR are either inaccurate or excessively time-consuming, and are therefore incapable of solving multi-hour SDNRs for large distribution networks. In this regard, a switch opening and exchange (SOE) method is proposed. Starting from a looped network with all switches closed, the SOE consists of three steps. The first step is to sequentially open the switches until all of the loops are opened. The second and third steps modify the status of branches obtained in the first step to obtain better radial topologies. Five test systems are used to validate the accuracy and fast solution speed of the SOE and the superiority of multi-hour SDNR over single-hour deterministic DNR.

With the large-scale integration of renewable energy sources and the growing frequency of extreme weather events, distribution networks are confronted with the dual imperative of achieving both economic efficiency and operational resilience. To address this challenge, a two-stage coordinated planning and scheduling framework integrating soft open points (SOPs) and mobile energy storage (MES) is proposed. In Stage I, the optimal capacities and installation locations of SOPs, along with the capacities of MES units, are determined under full lifecycle cost constraints. In Stage II, optimal scheduling strategies are formulated under annual multi-scenario conditions, aiming to optimize economic efficiency under normal scenarios and enhance operational resilience under fault scenarios. The framework incorporates displacement-energy coupling constraints for MES and a power flow control model for SOP, while explicitly considering load variability, renewable generation uncertainty, and topology reconfigurations caused by extreme events. Case studies conducted on an enhanced IEEE 33-bus system demonstrate that the proposed approach significantly reduces total operational costs and strengthens system resilience under normal and fault scenarios, providing a practical and scalable solution for the coordinated control of distributed energy resources.

Distributed photovoltaic (PV) in the distribution network accounted for an increasing proportion of the distribution network, and the power quality of the distribution network of the power quality problem is more and more significant. In this paper, the voltage regulation methods for low-voltage distribution networks containing high-penetration PV are investigated. First, the working principles of the four voltage control methods are introduced: energy storage system configuration, regulating the reactive power output of PV inverters, restricting the active power output of PV, adjusting the switching positions of on-load regulator trap changer and distribution network reconfiguration, and then, in combination with the recent related research, the optimization of each method is compared horizontally with its respective concerns and characteristics. The optimization of each method is compared horizontally with the recent studies to find out the focus and characteristics of each method, and the shortcomings of each method are explored. Coordinated voltage control through multiple flexibility resources has become the mainstream voltage regulation scheme, and distribution network voltage regulation is considered from the perspective of flexibility resources. The three types of flexibility resources, namely, source, network, and storage, have been widely used in distribution network voltage regulation. Although load-side resources have become one of the main regulation resources of the new type of power system, the current study introduces less about the participation of load-side flexibility resources in voltage regulation of LV distribution networks and advancing the application of load-side resources in voltage regulation of LV distribution networks is the focus of future research. Then, the important role of load-side flexibility resources in voltage regulation is described in three parts, namely, the important role of load-side resources, the development trend, and the suggestions for promoting the coordination of source-network-load-storage flexibility resources, aiming to promote the application of load-side resources in voltage regulation in LV distribution networks, and the suggestions and programs are proposed for the technological challenges faced by voltage regulation. In the context of today’s new power system emphasizing the interaction of source, network, load, and storage, new technologies and methods for solving voltage problems in LV distribution networks are prospected, with a view to providing certain reference value for the actual operation and optimization of distribution network systems.