No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Optimization method for short circuit current reduction in extensive meshed LV network
... A. Amin et al. conducted an optimal reconfiguration study with an enhanced Brute-Force algorithm to reduce fault currents in power systems, considering both steady-state stability and generator rotor angle stability [14]. D. Topolanek et al. have studied the reduction of fault currents on a real distribution system through reconfiguration approaches involving bus splitting and area separation methods [15]. In [16], an algorithm based on Particle Swarm Optimization (PSO) is used for the optimal reconfiguration of IEEE 83-Bus distribution system to mitigate fault currents within the suitable voltage profiles of the buses. ...
This study investigates the use of network reconfiguration as a cost-effective method to optimize power system performance through the minimization of fault currents and power losses. In single-objective optimizations, the study targets the reduction of the average fault current of the buses and the power losses individually. Additionally, a multi-objective optimization study is conducted to address both parameters simultaneously. Optimization scenarios are applied on 33-bus test system through Walrus Optimizer. The results demonstrate that reconfiguration can significantly reduce power losses and fault currents, compared to the base configuration of the test system, which had a power loss of 202.60 kW and an average fault current of 2.60 p.u. Single-objective optimizations reduced power losses to 139.551 kW and minimized average fault current to 2.13 p.u. Furthermore, the multi-objective optimization provided a range of Pareto optimal solutions, examining both criteria and highlighting the flexibility of reconfiguration in adapting to power system needs.
... Optimizing a large active distribution network with multiple control assets has also been challenging. Distributed and decentralized optimization methods are proposed for resource-efficient and reliable computation of the optimal operating states in a distribution network [19][20][21][22]. In addition, researchers have also highlighted the benefit of using controllable assets at different voltage levels for the optimal functioning of a distribution network [20,23,24]. ...
An increase in the share of weather-dependent generation at low voltage levels necessitates incorporating the low-voltage network in optimizing a distribution network. Optimization in a multi-voltage network requires significant computation time and effort due to many nodes operating at different voltage levels. This research proposes a decomposition and strategic optimization method to reduce the computation requirements for such large multi-voltage distribution networks. The proposed algorithm reduces the space complexity and the computation time required for solving the optimization routines of these multi-voltage distribution networks. A virtual transformer model incorporates tap-changer as a continuous variable in the semidefinite programming power flow optimization model. The zero-duality gap condition for multiple virtual transformers is proven empirically. Compared to a centralized optimization using the same power flow model, the proposed framework reduced the computation time by 96%.
... Also, in the case of an existing power system, fault studies are essential in the events of system expansion and design of protection systems [1] - [3]. Various fault categories are present within power systems, broadly classified as symmetrical and unsymmetrical faults [4] - [8]. Determination of currents and voltage waveforms arising from various faults occurring at different locations within the power system network is essential [9], [10]. ...
Hybrid power systems, integrating renewable energy sources, energy storage devices, and
conventional generators, are at the vanguard of the transition towards sustainable energy
solutions. However, ensuring the robustness and reliability of these systems under fault
conditions, particularly short circuit faults, remains a challenge. This research explores the
complexity of hybrid networks, conducting a comprehensive transient analysis of short-circuit
faults to enhance the understanding of their impact. The study employs advanced simulation
techniques and state-of-the-art modeling approaches to examine the dynamic behavior of hybrid
networks during fault events. Through detailed investigations, this research aims to unravel the
transient responses of various fault locations, including fault resistance, and transmission line
length, renewable energy sources, when subjected to short circuit faults. The findings of this
study not only contribute to the academic discourse surrounding hybrid power systems but also
provide invaluable insights for industry practitioners, guiding the development of reliable and
efficient hybrid energy infrastructures for a sustainable future.
This paper presents a distributed voltage regulation method based on multi-agent system control and network self-organization for a large distribution network. The network autonomously organizes itself into small subnetworks through the epsilon decomposition of the sensitivity matrix, and agents group themselves into these subnetworks with the communication links being autonomously determined. Each subnetwork controls its voltage by locating the closest local distributed generation and optimizing their outputs. This simplifies and reduces the size of the optimization problem and the interaction requirements. This approach also facilitates adaptive grouping of the network by self-reorganizing to maintain a stable state in response to time-varying network requirements and changes. The effectiveness of the proposed approach is validated through simulations on a model of a real heavily-meshed secondary distribution network. Simulation results and comparisons with other methods demonstrate the ability of the subnetworks to autonomously and independently regulate the voltage and to adapt to unpredictable network conditions over time, thereby enabling autonomous and flexible distribution networks.
Electrical distribution networks are facing an energy transition which entails an increase of decentralised renewable energy sources and electric vehicles. The resulting temporal and spatial uncertainty in the generation/load patterns challenges the operations of an infrastructure not designed for such a transition. In this situation, Optimal Power Flow methods can play a key role in identifying system weak points and supporting efficient management of the electrical networks, including the distribution level. In this work, to support distribution system operators’ decision-making process, we aim at attaining a quasi-optimal solution in the shortest time possible in an electrical network experiencing a large growth of distributed energy sources. We propose an optimisation method based on a modified version of a genetic algorithm and the Python pandapower package. The method is tested on a model of a real urban meshed network of a large Czech city. The optimisation method minimises the total operating costs of the distribution network by controlling selected network components and parameters, namely the transformer tap changers and the active power demand at consumption nodes. The results of our method are compared with the exact solution showing that a close-to-optimal solution of the observed problem can be reached in a relatively short time.
Next to building insulation, heat pumps driven by electrical compressors (eHPs) or by gas engines (geHPs) can be used to reduce primary energy demand for heating. They come with different investment requirements, operating costs and emissions caused. In addition, they affect both the power and gas grids, which necessitates the assessment of both infrastructures regarding grid expansion planning. To calculate costs and CO2 emissions, 2000 electrical load profiles and 180 different heat demand profiles for single-family homes were simulated and heat pump models were applied. In a case study for a neighborhood energy model, the load profiles were assigned to buildings in an example town using public data on locations, building age and energetic refurbishment variants. In addition, the town’s gas distribution network and low voltage grid were modeled. Power and gas flows were simulated and costs for required grid extensions were calculated for 11% and 16% heat pump penetration. It was found that eHPs have the highest energy costs but will also have the lowest CO2 emissions by 2030 and 2050. For the investigated case, power grid investments of 11,800 euros/year are relatively low compared to gas grid connection costs of 70,400 euros/year. If eHPs and geHPs are combined, a slight reduction of overall costs is possible, but emissions would rise strongly compared to the all-electric case.
Distribution networks are subject to various faults and volatilities, including man-made faults and environmental disasters. Besides, perpetual load demand increase, inserting distributed generations (DGs), and networks interlinking not only complicated the network, but also increased the level of short circuit current. There are some methods in place to deal with this issue. Network restructuring, changing existing circuit breakers (CBs) with a higher rating one, and using fault current limiters (FCLs) are as proper approaches. In this paper, an optimal allocation of FCLs is proposed to mitigate the fault current into the permissible level. Non-dominated sorting genetic algorithm II (NSGA-II) is used to optimize the objectives function in MATLAB software. IEEE 34 node test feeder is used to check the performance of the proposed method.
The interaction of the smart transformer (ST) with the grid-converter-based distributed energy resources (DERs) could trigger instability. Recent research efforts have been made to study the stability issues caused by the resonances (e.g. LC or LCL filter resonances of DERs) in ST LV side grid. This paper studies the impact of the grid synchronization of the grid-converter-based DER on the stability of the ST as well as the grid. The equivalent admittance of the grid converter considering the effect of synchronous reference frame phase-locked loop (SRF-PLL) is developed and merged with the ST voltage control. Based on the generalized Nyquist criterion, the stability analysis of a ST-fed grid is carried out, showing that the PLL bandwidth is a key factor that determines the system stability. To address the stability issue, a stabilization method based on virtual impedance is proposed and can be seamlessly integrated with the ST voltage control. Simulation and experimental results are provided to validate the effectiveness of the analysis and the proposed control strategy.
Python has become the programming language of choice for research and industry projects related to data science, machine learning, and deep learning. Since optimization is an inherent part of these research fields, more optimization related frameworks have arisen in the past few years. Only a few of them support optimization of multiple conflicting objectives at a time, but do not provide comprehensive tools for a complete multi-objective optimization task. To address this issue, we have developed pymoo, a multiobjective optimization framework in Python. We provide a guide to getting started with our framework by demonstrating the implementation of an exemplary constrained multi-objective optimization scenario. Moreover, we give a high-level overview of the architecture of pymoo to show its capabilities followed by an explanation of each module and its corresponding sub-modules. The implementations in our framework are customizable and algorithms can be modified/extended by supplying custom operators. Moreover, a variety of single, multi- and many-objective test problems are provided and gradients can be retrieved by automatic differentiation out of the box. Also, pymoo addresses practical needs, such as the parallelization of function evaluations, methods to visualize low and high-dimensional spaces, and tools for multi-criteria decision making. For more information about pymoo, readers are encouraged to visit: https://pymoo.org.
The gradual fossil fuel depletion, the exponential increase in load, the environmental, social and political concern has lead to the gradual sprawl of distributed energy resources in the form of microgrid. But disproportionate integration of DERs into the conventional power system may result in the anomalous system behavior causing violation of operational limits i.e., under and overvoltage, thermal overloading, line losses, transformer or feeder overloading, harmonic distortion breaching the standards, mal-operation of the protective devices etc. The key to mitigate these violations is appropriate hosting capacity assessment. This paper proposes the concept of customary hosting capacity and enhanced hosting capacity at all nodes in a network with low voltage level, high voltage level, ampacity and total harmonic distortion as the governing criteria. The analysis is tested on reconfigured IEEE 33 bus distribution system, whose reconfiguration is achieved by particle swarm optimization, keeping loss minimization as the objective function. The attainment of enhanced hosting capacity is significant since it may lead to considerable improvement of voltage in the network, maintaining the line current values very low. The experimentation on reconfigured and base configuration of IEEE 33 bus distribution system is performed in MATLAB and validated in hardware in loop. The results are also compared with existing literature to prove its efficacy.
This article outlines a procedure for loss allocation in both radial and meshed distribution networks with distributed generation that could be regulated in various ways. The method is analytically developed based on the theory of electrical circuits combined with game theory based on Aumann-Shapley, which guarantees both the electrical principles and the fair axioms of game theory. The proposed method obtains unitary participation coefficients for each network user based on the currents demanded/injected by each user and the network topology. The proposed allocation method based on Aumann-Shapley has been compared with other traditional allocation methods, is adaptable to distribution networks, and shows great potential and ease of implementation. Moreover, it can be applied to any kind of distribution network (radial or meshed) with distributed energy resources.
In this paper, we study multiple interconnected smart microgrids, and develop an efficient strategy for their internal device scheduling and energy trading. The paper achieves the following objectives. Firstly, each microgrid holistic-ally manages its internal devices such as local storage systems and reactive power compensation levels, and external active/ reactive energy trading (with the external grid and other micro-grids) simultaneously for real-time optimization. Scenarios of social cooperation and game in interconnected microgrids are formulated as distinct objectives of the microgrids, which aim to optimize their own performance and gain benefits through energy trading. Secondly, microgrids of different network topologies (radial and meshed) and nominal voltages are fully incorporated. Thirdly, a fully distributed model-predictive and computational-intelligence based algorithm is proposed, so that microgrids’ devices operate autonomously with minimum communication exchange, obviating the need for a central controller. Convergence properties of the proposed distributed algorithm are analyzed and benchmarked with a state-of-the-art distributed algorithm, and numerical simulations for different scenarios are performed demonstrating that the proposed dist-ributed strategy can be feasibly applied to real-world microgrids.
A practical loss allocation method should consider size, distance from source, and network usage of its’ participants. The power flow pattern in distribution network has changed over a period due to increased integration of distributed generation and hence, the equivalent distance and usage of network has changed for the conventional distribution network. The regulators require a fair and robust model for loss allocation which should be able to discriminate between the contributions of multiple participants. The loss allocation techniques proposed earlier in the literature either require additional quantities over and above power flow solution or are specifically developed only for radial distribution networks. This paper presents a cooperative game approach which provides stable solution integrated with appropriate penalties or rewards to participants. The proposed method provides an analytical solution which is solved using Shapley value for radial as well as weakly meshed distribution network. The memory and computational burden with the Shapley value have been overcome by a final analytical formula that satisfies the axioms of fairness. The use of the method is straightforward without any assumptions and requires only network data and power flow solution. The results are illustrated using different scenarios of DG output levels and network topologies and compared with other methods to establish its effectiveness.
An algorithm for the optimal voltage regulation of distribution secondary networks with distributed generators (DGs) is proposed in the paper. Based on the decomposition of the sensitivity matrix (inverse of Jacobian) obtained from the solution of the Newton-Raphson power flow problem, a large secondary network is divided into several small subnetworks. From the decomposition, the range of influence of each DG on the voltage of the entire network is determined. When voltage at particular nodes exceeds normal operating limits, the nearest DGs can be located and commanded to control the voltage. The control action can be coordinated using communications in a small-size subnetwork. The voltage regulation is achieved by solving a small linear programming optimization problem with an objective function that makes every DG to optimize its generation. The algorithm is tested with a model of a real heavily-meshed secondary network. The results show that the algorithm proposed in this paper can effectively control the voltage in a distributed manner. It is also discussed in the paper how to choose the value of for the system decomposition.
We propose a new graph theory-based algorithm for the determination of the minimum breakpoint set of meshed systems. We show that if there is a k-tree in the graph G ( V , E ) associated with the system, then the minimum breakpoint set has a cardinality of | E |-| V |+ k . The proposed algorithm implements an iterative procedure to identify such a k-tree and having minimum k . It is found that the cardinality of the minimum breakpoint set is 9 for the IEEE 14-bus system and 16 for the IEEE 30-bus system. These minimum values are smaller than the previously best reported results of 10 and 18 for the same systems.
This tutorial covers the canonical genetic algorithm as well as more experimental forms of genetic algorithms, including parallel island models and parallel cellular genetic algorithms. The tutorial also illustrates genetic search by hyperplane sampling. The theoretical foundations of genetic algorithms are reviewed, include the schema theorem as well as recently developed exact models of the canonical genetic algorithm. Keywords: Genetic Algorithms, Search, Parallel Algorithms 1 Introduction Genetic Algorithms are a family of computational models inspired by evolution. These algorithms encode a potential solution to a specific problem on a simple chromosome-like data structure and apply recombination operators to these structures so as to preserve critical information. Genetic algorithms are often viewed as function optimizers, although the range of problems to which genetic algorithms have been applied is quite broad. An implementation of a genetic algorithm begins with a pop...
The increasing penetration from intermittent renewable distributed energy resources in distribution grid brings along challenges in grid operation and planning. To evaluate the impact on the grid voltage profile, grid losses, and discrete actions from assets (e.g. transformer tap changes), quasi-static simulation is an appropriate method. Quasi-static time series and Monte-Carlo simulation requires a tremendous number of power flow calculations (PFCs), which can be significantly accelerated with a parallel High-Performance Computing (HPC)-PFC solver. In this paper, we propose a HPC-PFC-solver-based grid simulation (parallel simulation) approach for a multi-core CPU platform as well as a greedy method, which can prevent the errors caused by simultaneous parallel simulation. The performance of the proposed approach and the comparison is demonstrated with two use cases.
Based on
-value concept of cooperative game theory, this paper presents a technique for efficient loss allocation in radial and weakly meshed distribution network (DN). The application of
-value concept requires the quasi-balanced condition to be satisfied by the game. Therefore, with suitable mathematical derivations, it is demonstrated that a DN having only load or only distributed generation (DG) satisfies the necessary quasi-balanced condition. Then, a two-step method to allocate the network losses among loads and DGs is presented. Further, the intensive computational burden involved in
-value method limits its applicability to practical DN. Therefore, by suitable mathematical analysis, computational complexity associated with
-value method is reduced. The proposed method is tested on a few possible scenarios of loading, DG penetration, DG location, and X/R ratio in radial and weakly meshed DNs. The results obtained by the proposed
-value, conventional
-value, Shapley value and branch current decomposition method are compared based on the attributes of fair and efficient loss allocation.
This letter proposes a fully decentralized approach to economic dispatch in microgrids with a novel communicationless strategy. It is aimed to overcome the limitations on strong dependency of center node and nodal communication in the conventional approaches. Comparative case studies based on a 14-bus microgrid demonstrate that the proposed approach can achieve a global consensus in a mesh network and the overall communication burden can be significantly reduced.
To achieve high reliability, the urban distribution networks are mesh-constructed and radial-operated, in which the outage load can be restored to adjacent feeders via tie-lines after faults. Conventionally, iterative optimization-simulation methods and heuristics are adopted for distribution network planning, which cannot guarantee global optimality. Besides, existing reliability-constrained planning model cannot explicitly assess the reliability indices for mesh distribution networks, so the resulted plan scheme may be overly invested. In this paper, we propose a novel multistage expansion planning model for mesh distribution networks, in which reliability assessment is explicitly implemented as constraints. The different investment/reliability preferences for buses are also customized. Specifically, post-fault load restoration between feeders through tie-lines is modeled as a case of post-fault network reconfiguration. The planning model is then cast as an instance of mixed-integer linear programming and can be effectively solved by off-the-shelf solvers. We use a 54-node system to test the performance of proposed model. Simulation results show the effectiveness and flexibility of this methodology.
Graphical models are a succinct way to represent the structure in probability distributions. This article analyzes the graphical model of nodal voltages in non-radial power distribution grids. Using algebraic and structural properties of graphical models, algorithms exactly determining topology and detecting line changes for distribution grids are presented along with their theoretical limitations. We show that if distribution grids have cycles/loops of size greater than three, then nodal voltages are sufficient for efficient topology estimation without additional assumptions on system parameters. In contrast, line failure or change detection using nodal voltages does not require any structural assumption. Under noisy measurements, we provide the first non-trivial bounds on the maximum noise that the system can tolerate for asymptotically correct topology recovery. The performance of the designed algorithms is validated with non-linear AC power flow samples generated by Matpower on test grids, including scenarios with injection correlations and system noise.
preprint @ https://arxiv.org/abs/1711.03331
Pandapower is a Python based, BSD-licensed power system analysis tool aimed at automation of static and quasi-static analysis and optimization of power systems. It is a full fledged power system analysis tool that provides power flow, optimal power flow, state estimation, topological graph searches and short circuit calculations according to IEC 60909. The pandapower network model is based on electric elements, which are defined by nameplate parameters and internally processed with equivalent circuit models. The tabular data structure used to define networks is based on the Python library pandas, which allows comfortable handling of input and output parameters. The implementation in Python makes pandapower easy to use and allows comfortable extension with third-party libraries. pandapower has been successfully applied in several grid studies and validated with real grid data.
Transition to a sustainable energy environment results in aggregated generator and load dynamics in the distribution network. State estimation is a key function in building adequate network models for on-line monitoring and analyses. The requirements of Distribution System State Estimation (DSSE) is becoming stringent because of the needs of new system modeling and operation practices associated with integration of distributed energy resources and the adoption of advanced technologies in distribution network. This paper summarizes the state of the art technology, major hurdles and challenges in DSSE development. The opportunities, paradigm shift and future research directions that could facilitate the need of DSSE are discussed.
This paper discusses the feasibility of using custom-er coupon demand response in meshed secondary networks. Cus-tomers are rewarded by coupons to achieve the objective of opti-mal operation cost during peak periods. The interdependence of the locational marginal price and the demand is modeled by an artificial neural network. The effect of multiple load aggregators participating in customer coupon demand response is also inves-tigated. Because load aggregators satisfy different proportions of the objective, a fairness function is defined that guarantees that aggregators are rewarded in correspondence with their partici-pation towards the objective. Energy loss is also considered in the objective as it is an essential part of the distribution system. A dynamic coupon mechanism is designed to cope with the chang-ing nature of the demand. To validate the effectiveness of the method, simulations of the proposed method have been per-formed on a real heavily-meshed distribution network in this paper. The results show that customer coupon demand response significantly contributes to shaving the peak, therefore, bringing considerable economic savings and reduction of loss.
This paper presents a framework for analyzing the resilience of an electric power grid with integrated microgrids in extreme conditions. The objective of this paper is to demonstrate that controllable and islandable microgrids can help improve the resiliency of power grids in extreme conditions. Four resilience indices are introduced to measure the impact of extreme events. Index K measures the expected number of lines on outage due to extreme events. Index loss of load probability measures the probability of load not being fully supplied. Index expected demand not supplied measures the expected demand that cannot be supplied. Index G measures the difficulty level of grid recovery. The mechanism of extreme events affecting power grid operation is analyzed based on the proposed mesh grid approach. The relationship among transmission grid, distribution grid, and microgrid in extreme conditions is discussed. The Markov chain is utilized to represent the state transition of a power grid with integrated microgrids in extreme conditions. The Monte Carlo method is employed to calculate the resilience indices. The proposed power grid resilience analysis framework is demonstrated using the IEEE 30-bus and 118-bus systems assuming all loads are within microgrids.
In some mature power systems located in densely populated areas, an increase in demand and supply is resulting in very high short-circuit levels that are either too close to or over the safe breaker operating limits. Fault current limiters (FCLs) built with advanced materials and methods are a potent solution in this situation wherein an in-line FCL offers a very low impedance and power loss under normal operating conditions, but a high impedance and hence a lower short-circuit current during faults. From a planning perspective for complex meshed networks where faults are concurrently fed from several sources, it is important to optimally locate and size FCLs, which is a difficult mixed integer nonlinear optimization challenge as commonly used location sensitivity index methods and heuristic search techniques seem inadequate. In this paper, an iterative mixed integer nonlinear optimization method is proposed to optimally locate and size FCLs in a power system by searching the entire solution space such that costs are the least and fault currents are curtailed to levels within breakers' limits, without prior knowledge of the best locations irrespective of the size of the system. The IEEE 9-bus, IEEE 30-bus, and a real North American 395-bus transmission system were chosen to test and demonstrate the proposed method.
This paper presents a mixed-integer linear optimization problem for unit commitment and economic dispatch of power generators in a meshed isolated power system. The optimization problem is referred to as the optimal reserve planning problem (ORPP). The ORPP guarantees that the system frequency is kept above a predefined limit in the event of a contingency. The minimum frequency constraints are formulated using novel sufficient conditions that take into account the system inertia and the dynamics of the power generators. The proposed sufficient conditions are attractive from both a computational and a modelling point of view. We compare the ORPP to a unit commitment problem that only considers the stationary behavior of the frequency. Simulations based on a Faroe Islands case study show that, without being overly conservative, potential blackouts and power outages can be avoided using the ORPP. In the particular case study, the cost increase associated with the additional security provided by the ORPP is less than 3%.
The disadvantage of multi-objective estimation of distribution algorithm (MEDA) is insufficiency in the local search capability, but its performance is significantly improve after introduce bacterial forging behavior. It is more excellent than NSGA-2 and MEDA. This paper combines the sensitivity coefficient of all branches when short circuit is happening in power system and MEDA-BF, then puts forward an excellent arrange scheme that application the minimum number of superconducting fault current limiters (SFCL) and more economics. First of all, we need to calculate sensitivity coefficient of all branches in the power system. Then, we chose candidate branches base on their sensitivity coefficient. At last, we apply the MEDA-BF optimal allocating SFCL with the number and economic of installation SFCL as objective functions.
Due to increasing neutral current and power losses resulting from phase unbalancing in distribution networks, the act of phase balancing has been a matter of interest in recent years. In this paper, a new method for phase arrangement of laterals and the distribution transformers is presented based on bacterial foraging (BF) oriented by particle swarm optimization (PSO) algorithm (BF-PSO). The algorithm is proposed for radial and meshed distribution networks in the presence of unbalanced loads. The objective function of this problem includes the neutral current of the supporting feeder, the rephasing cost, the voltage drop, and the line losses. Since these objectives do not have similar units and the same variation ranges, the four objectives are fuzzified and then integrated as the fuzzy multi-objective function. In order to prove the efficiency of the BF-PSO algorithm, its performance is compared with bacterial foraging, genetic and immune algorithms. To evaluate the proposed method, it is applied to feeder No. 3062 in Ahwaz, Iran. The simulation results confirm the efficiency of the method for the reduction of system costs and network phase balancing.
An optimal Strategy based on multilevel fuzzy comprehensive evaluation model in restricting short circuit current is presented. This method starts with establishing hierarchical structural diagram of short circuit current limitation measures and multilevel fuzzy comprehensive evaluation model based on Analytic Hierarchy Process. Then the weight values of different levels can be determined by setting up a judgment matrix. Finally, the short circuit current level, steady-state security, transient stability and operation economy is taken into account by introducing fuzzy behavior indices and membership degree function. The limitation scheme can be evaluated systematically, comprehensivly and quantificationally through multi-level fuzzy comprehensive evaluation. Simulation is carried out by taking Jingjintang power grid as an example, the feasibility and practicality of the proposed method is verified.
An optimization model for deploying short circuit current limiters is presented. The problem is modelled as a 0-1 mixed integer programming problem. The mathematic model is solved by a discrete particle swarm optimization algorithm (DPSO). The results of a 107-bus system in East China are provided to validate the possible applications of the proposed method.
In this paper, a new concept of restorability of power supply (RPS) and the corresponding performance indices are suggested. Load transfer algorithm is used for enhancing the RPS of distribution systems. Feeder incidence diagram (FID) is proposed for analyzing the rules for load transfer. The performance indices, maximal interrupted power (MIP), and maximal number of interrupted customers (MNIC), is calculated before the restoration operation or apparatus maintenance. These indices can serve for evaluating the operation status of feeders, estimating the impact of load transfer and switch operation, and assessing the RPS. They can also be used to optimize the allocation of tie-breakers in a meshed network. The work is of great significance in distribution system reconstruction in China.
Meshed LV distribution network
- Topolanek
HEBO: Heteroscedastic Evolutionary Bayesian Optimisation
- A Cowen-Rivers