Optimal Location and Sizing of Distributed Generator via Improved Multi-Objective Particle Swarm Optimization in Active Distribution Network Considering Multi-Resource

In the framework of vigorous promotion of low-carbon power system growth as well as economic globalization,multi-resource penetration in active distribution networks has been advancing fiercely.In particular,distributed generation(DG)based on renewable energy is critical for active distribution network operation enhancement.To comprehensively analyze the accessing impact of DG in distribution networks from various parts,this paper establishes an optimal DG location and sizing planning model based on active power losses,voltage profile,pollution emissions,and the economics of DG costs as well as meteorological conditions.Subsequently,multiobjective particle swarm optimization(MOPSO)is applied to obtain the optimal Pareto front.Besides,for the sake of avoiding the influence of the subjective setting of the weight coefficient,the decisionmethod based on amodified ideal point is applied to execute a Pareto front decision.Finally,simulation tests based on IEEE33 and IEEE69 nodes are designed.The experimental results show thatMOPSO can achieve wider and more uniformPareto front distribution.In the IEEE33 node test system,power loss,and voltage deviation decreased by 52.23%,and 38.89%,respectively,while taking the economy into account.In the IEEE69 test system,the three indexes decreased by 19.67%,and 58.96%,respectively.

Blockchain-Based Power Transaction Method for Active Distribution Network

A blockchain-based power transaction method is proposed for Active Distribution Network(ADN),considering the poor security and high cost of a centralized power trading system.Firstly,the decentralized blockchain structure of the ADN power transaction is built and the transaction information is kept in blocks.Secondly,considering the transaction needs between users and power suppliers in ADN,an energy request mechanism is proposed,and the optimization objective function is designed by integrating cost aware requests and storage aware requests.Finally,the particle swarm optimization algorithm is used for multi-objective optimal search to find the power trading scheme with the minimum power purchase cost of users and the maximum power sold by power suppliers.The experimental demonstration of the proposed method based on the experimental platform shows that when the number of participants is no more than 10,the transaction delay time is 0.2 s,and the transaction cost fluctuates at 200,000 yuan,which is better than other comparison methods.

Leader-follower Optimal Selection Method for Distributed Control System in Active Distribution Networks

Aiming at the shortcomings of a traditional centralized control in an active distribution network(AND),this paper proposes a leader-follower distributed group cooperative control strategy to realize multiple operation and control tasks for an ADN.The distributed information exchange protocols of the distributed generation(DG)group devoted to node voltage regulation or exchange power control are developed using a DG power utilization ratio as the consensus variable.On these bases,this study further investigates the leader optimal selection method for a DG group to improve the response speed of the distributed control system.Furthermore,a single or multiple leader selection model is established to minimize the constraints of the one-step convergence factor and the number of leaders to improve the response speed of the distributed control system.The simulation results of the IEEE 33 bus standard test system show the effectiveness of the proposed distributed control strategy.In addition,the response speed of a DG control group can be improved effectively when the single or multiple leaders are selected optimally.

Real-time Operation Optimization in Active Distribution Networks Based on Multi-agent Deep Reinforcement Learning

The increasing integration of intermittent renewable energy sources(RESs)poses great challenges to active distribution networks(ADNs),such as frequent voltage fluctuations.This paper proposes a novel ADN strategy based on multiagent deep reinforcement learning(MADRL),which harnesses the regulating function of switch state transitions for the realtime voltage regulation and loss minimization.After deploying the calculated optimal switch topologies,the distribution network operator will dynamically adjust the distributed energy resources(DERs)to enhance the operation performance of ADNs based on the policies trained by the MADRL algorithm.Owing to the model-free characteristics and the generalization of deep reinforcement learning,the proposed strategy can still achieve optimization objectives even when applied to similar but unseen environments.Additionally,integrating parameter sharing(PS)and prioritized experience replay(PER)mechanisms substantially improves the strategic performance and scalability.This framework has been tested on modified IEEE 33-bus,IEEE 118-bus,and three-phase unbalanced 123-bus systems.The results demonstrate the significant real-time regulation capabilities of the proposed strategy.

A Privacy-preserving Energy Management System Based on Homomorphic Cryptosystem for IoT-enabled Active Distribution Network

Active distribution network(ADN),as a typically cyber-physical system,develops with the evolution of Internet of Things(IoTs),which makes the network vulnerable to cybersecurity threats.In this paper,the eavesdropping attacks that lead to privacy breaches are addressed for the IoT-enabled ADN.A privacy-preserving energy management system(EMS)is proposed and empowered by secure data exchange protocols based on the homomorphic cryptosystem.During the information transmission among distributed generators and load customers in the EMS,private information including power usage and electricity bidding price can be effectively protected against eavesdropping attacks.The correctness of the final solutions,e.g.,optimal market clearing price and unified power utilization ratio,can be deterministically guaranteed.The simulation results demonstrate the effectiveness and the computational efficiency of the proposed homomorphically encrypted EMS.

Joint Planning of Active Distribution Network and EV Charging Stations Considering Vehicle-to-Grid Functionality and Reactive Power Support

This paper proposes a collaborative planning model for active distribution network(ADN)and electric vehicle(EV)charging stations that fully considers vehicle-to-grid(V2G)function and reactive power support of EVs in different regions.This paper employs a sequential decomposition method based on physical characteristics of the problem,breaking down the holistic problem into two sub-problems for solution.Subproblem I optimizes the charging and discharging behavior of autopilot electric vehicles(AEVs)using a mixed-integer linear programming(MILP)model.Subproblem II uses a mixed-integer secondorder cone programming(MISOCP)model to plan ADN and retrofit or construct V2G charging stations(V2GCS),as well as multiple distributed generation resources(DGRs).The paper also analyzes the impact of bi-directional active-reactive power interaction of V2GCS on ADN planning.The presented model is tested in the 47-node ADN in Longgang District,Shenzhen,China,and the IEEE 33-node ADN,demonstrating that decomposition can significantly improve the speed of solving large-scale problems while maintaining accuracy with low AEV penetration.

Two-stage Stochastic Programming for Coordinated Operation of Distributed Energy Resources in Unbalanced Active Distribution Networks with Diverse Correlated Uncertainties

This paper proposes a stochastic programming(SP)method for coordinated operation of distributed energy resources(DERs)in the unbalanced active distribution network(ADN)with diverse correlated uncertainties.First,the threephase branch flow is modeled to characterize the unbalanced nature of the ADN,schedule DER for three phases,and derive a realistic DER allocation.Then,both active and reactive power resources are co-optimized for voltage regulation and power loss reduction.Second,the battery degradation is considered to model the aging cost for each charging or discharging event,leading to a more realistic cost estimation.Further,copulabased uncertainty modeling is applied to capture the correlations between renewable generation and power loads,and the twostage SP method is then used to get final solutions.Finally,numerical case studies are conducted on an IEEE 34-bus three-phase ADN,verifying that the proposed method can effectively reduce the system cost and co-optimize the active and reactive power.

A Two-stage Stochastic Mixed-integer Programming Model for Resilience Enhancement of Active Distribution Networks

Most existing distribution networks are difficult to withstand the impact of meteorological disasters. With the development of active distribution networks(ADNs), more and more upgrading and updating resources are applied to enhance the resilience of ADNs. A two-stage stochastic mixed-integer programming(SMIP) model is proposed in this paper to minimize the upgrading and operation cost of ADNs by considering random scenarios referring to different operation scenarios of ADNs caused by disastrous weather events. In the first stage, the planning decision is formulated according to the measures of hardening existing distribution lines, upgrading automatic switches, and deploying energy storage resources. The second stage is to evaluate the operation cost of ADNs by considering the cost of load shedding due to disastrous weather and optimal deployment of energy storage systems(ESSs) under normal weather condition. A novel modeling method is proposed to address the uncertainty of the operation state of distribution lines according to the canonical representation of logical constraints. The progressive hedging algorithm(PHA) is adopted to solve the SMIP model. The IEEE 33-node test system is employed to verify the feasibility and effectiveness of the proposed method. The results show that the proposed model can enhance the resilience of the ADN while ensuring economy.

Deep Reinforcement Learning Based Charging Scheduling for Household Electric Vehicles in Active Distribution Network

With the booming of electric vehicles(EVs) across the world, their increasing charging demands pose challenges to urban distribution networks. Particularly, due to the further implementation of time-of-use prices, the charging behaviors of household EVs are concentrated on low-cost periods, thus generating new load peaks and affecting the secure operation of the medium-and low-voltage grids. This problem is particularly acute in many old communities with relatively poor electricity infrastructure. In this paper, a novel two-stage charging scheduling scheme based on deep reinforcement learning is proposed to improve the power quality and achieve optimal charging scheduling of household EVs simultaneously in active distribution network(ADN) during valley period. In the first stage, the optimal charging profiles of charging stations are determined by solving the optimal power flow with the objective of eliminating peak-valley load differences. In the second stage, an intelligent agent based on proximal policy optimization algorithm is developed to dispatch the household EVs sequentially within the low-cost period considering their discrete nature of arrival. Through powerful approximation of neural network, the challenge of imperfect knowledge is tackled effectively during the charging scheduling process. Finally, numerical results demonstrate that the proposed scheme exhibits great improvement in relieving peak-valley differences as well as improving voltage quality in the ADN.

Voltage Profile Optimization of Active Distribution Networks Considering Dispatchable Capacity of 5G Base Station Backup Batteries

The penetration of distributed energy resources(DERs) and energy-intensive resources is gradually increasing in active distribution networks(ADNs), which leads to frequent and severe voltage violation problems. As a densely distributed flexible resource in the future distribution network, 5G base station(BS) backup battery is used to regulate the voltage profile of ADN in this paper. First, the dispatchable potential of 5G BS backup batteries is analyzed. Considering the spatial-temporal characteristics of electric load for 5G BS, the dispatchable capacity of backup batteries at different time intervals is evaluated based on historical heat map data. Then, a voltage profile optimization model for ADN is established, consisting of 5G BS backup batteries and other voltage regulation resources. In this model, the charging/discharging behavior of backup batteries is based on its evaluation result of dispatchable capacity. Finally, the range of charging/discharging cost coefficients of 5G BS that benefits ADN and 5G operators are analyzed respectively. Further, an incentive policy for 5G operators is proposed. Under this policy, the charging/discharging cost coefficients of 5G BS can achieve a win-win situation for ADN and 5G operators. As an emerging flexible resource in ADN, the effectiveness and economy of 5G BS backup batteries participating in voltage profile optimization are verified in a test distribution network.

Distributed processing based fault location,isolation,and service restoration method for active distribution network

Active distribution network(ADN)is a solution for power system with interconnection of distributed energy resources(DER),which may change the network operation and power flow of traditional power distribution network.However,in some circumstances the malfunction of protection and feeder automation in distribution network occurs due to the uncertain bidirectional power flow.Therefore,a novel method of fault location,isolation,and service restoration(FLISR)for ADN based on distributed processing is proposed in this paper.The differential-activated algorithm based on synchronous sampling for feeder fault location and isolation is studied,and a framework of fault restoration is established for ADN.Finally,the effectiveness of the proposed algorithm is verified via computer simulation of a case study for active distributed power system.

Robust Optimal Operation of Active Distribution Network Based on Minimum Confidence Interval of Distributed Energy Beta Distribution

With the gradual increase of distributed energy penetration,the traditional optimization model of distribution network can no longer guarantee the stable and efficient operation of the distribution network.In order to deal with the inevitable uncertainty of distributed energy,a new robust optimal operation method is proposed for active distribution network(ADN)based on the minimum confidence interval of distributed energy Beta distribution in this paper.First,an ADN model is established with second-order cone to include the energy storage device,capacitor bank,static var compensator,on-load tap changer,wind turbine and photovoltaic.Then,the historical data of related distributed energy are analyzed and described by the probability density function,and the minimum confidence interval is obtained by interval searching.Furthermore,via taking this minimum confidence interval as the uncertain interval,a less conservative two-stage robust optimization model is established and solved for ADN.The simulation results for the IEEE33-bus distribution network have verified that the proposed method can realize a more stable and efficient operation of the distribution network compared with the traditional robust optimization method.

Total Quadrant Security Region for Active Distribution Network with High Penetration of Distributed Generation

The region-based method has been applied in transmission systems and traditional passive distribution systems without power sources. This paper proposes the model of total quadrant security region(TQSR) for active distribution networks(ADN) with high penetration of distributed generation(DG). Firstly, TQSR is defined as a closed set of all the N-1 secure operation points in the state space of ADN. Then, the TQSR is modeled considering the constraints of state space,normal operation and N-1 security criterion. Then, the characteristics of TQSR are observed and analyzed on the test systems with different DG penetrations. TQSR can be located in any quadrant of the state space. For different DG penetrations,the shape and security features of TQSR are also different. Finally, the region map is discovered, which summarizes the features of different types of distribution networks.

Optimal Siting and Sizing of Distributed Renewable Energy in an Active Distribution Network

The distributed generation (DG) plays an important role in the context of the environmental problems and sustain- able development throughout the world. This paper proposes a DG siting and sizing model in an active distribution network (ADN). The objective is to minimize the total cost, including investment, operation and maintenance costs. The proposed model is transferred to a Mixed Integer Second-Order Cone Programming (MISOCP) model based on a distribution network forward backward-sweep power flow and constraint relaxation. The CVX platform and GUROBI solver are used for the solution. The scenario analysis is used for the uncertainties of load and DG. Different numbers of operational scenarios are considered in order to analyze the effect of a non-network solution to the final planning result and total investment. The planning results with and without consideration of active managements, and the planning results with and without taking environmental profits into consideration, are compared and analyzed. The proposed methodology is verified with a modified IEEE 33 example.

Multi-area distributed three-phase state estimation for unbalanced active distribution networks

This paper proposes a new multi-area framework for unbalanced active distribution network(ADN) state estimation. Firstly, an innovative three-phase distributed generator(DG) model is presented to take the asymmetric characteristics of DG three-phase outputs into consideration. Then a feasible method to set pseudo-measurements for unmonitored DGs is introduced. The states of DGs,together with the states of alternating current(AC) buses in ADNs, were estimated by using the weighted least squares(WLS) method. After that, the ADN was divided into several independent subareas. Based on the augmented Lagrangian method, this work proposes a fully distributed three-phase state estimator for the multi-area ADN.Finally, from the simulation results on the modified IEEE123-bus system, the effectiveness and applicability of the proposed methodology have been investigated and discussed.

Novel Affine Power Flow Method for Improving Accuracy of Interval Power Flow Data in Cyber Physical Systems of Active Distribution Networks

A large number of load power and power output of distributed generation in an active distribution network(ADN)are uncertain,which causes the classical affine power flow method to encounter problems of interval expansion and low efficiency when applied to an AND.This then leads to errors of interval power flow data sources in the cyber physical system(CPS)of an ADN.In order to improve the accuracy of interval power flow data in the CPS of an ADN,an affine power flow method of an ADN for restraining interval expansion is proposed.Aiming at the expansion of interval results caused by the approximation error of non-affine operations in an affine power flow method,the approximation method of the new noise source coefficient is improved,and it is proved that the improved method is superior to the classical method in restraining interval expansion.To overcome the decrease of computational efficiency caused by new noise sources,a novel merging method of new noise sources in an iterative process is designed.Simulation tests are conducted on an IEEE 33-bus,PG&E 69-bus and an actual 1180-bus system,which proves the validity of the proposed affine power flow method and its advantages in terms of computational efficiency and restraining interval expansion.

Intelligent Voltage Control Method in Active Distribution Networks Based on Averaged Weighted Double Deep Q-network Algorithm

High penetration of distributed renewable energy sources and electric vehicles(EVs)makes future active distribution network(ADN)highly variable.These characteristics put great challenges to traditional voltage control methods.Voltage control based on the deep Q-network(DQN)algorithm offers a potential solution to this problem because it possesses humanlevel control performance.However,the traditional DQN methods may produce overestimation of action reward values,resulting in degradation of obtained solutions.In this paper,an intelligent voltage control method based on averaged weighted double deep Q-network(AWDDQN)algorithm is proposed to overcome the shortcomings of overestimation of action reward values in DQN algorithm and underestimation of action reward values in double deep Q-network(DDQN)algorithm.Using the proposed method,the voltage control objective is incorporated into the designed action reward values and normalized to form a Markov decision process(MDP)model which is solved by the AWDDQN algorithm.The designed AWDDQN-based intelligent voltage control agent is trained offline and used as online intelligent dynamic voltage regulator for the ADN.The proposed voltage control method is validated using the IEEE 33-bus and 123-bus systems containing renewable energy sources and EVs,and compared with the DQN and DDQN algorithms based methods,and traditional mixed-integer nonlinear program based methods.The simulation results show that the proposed method has better convergence and less voltage volatility than the other ones.

Robust State Estimation of Active Distribution Networks with Multi-source Measurements

The volatile and intermittent nature of distributed generators(DGs) in active distribution networks(ADNs) increases the uncertainty of operating states. The introduction of distribution phasor measurement units(D-PMUs) enhances the monitoring level. The trade-offs of computational performance and robustness of state estimation in monitoring the network states are of great significance for ADNs with D-PMUs and DGs. This paper proposes a second-order cone programming(SOCP) based robust state estimation(RSE) method considering multi-source measurements. Firstly, a linearized state estimation model related to the SOCP state variables is formulated. The phase angle measurements of D-PMUs are converted to equivalent power measurements. Then, a revised SOCP-based RSE method with the weighted least absolute value estimator is proposed to enhance the convergence and bad data identification. Multi-time slots of D-PMU measurements are utilized to improve the estimation accuracy of RSE. Finally, the effectiveness of the proposed method is illustrated in the modified IEEE 33-node and IEEE 123-node systems.

Multi-stage Coordinated Robust Optimization for Soft Open Point Allocation in Active Distribution Networks with PV

To optimize the placement of soft open points(SOPs)in active distribution networks(ADNs),many aspects should be considered,including the adjustment of transmission power,integration of distributed generations(DGs),coordination with conventional control methods,and maintenance of economic costs.To address this multi-objective planning problem,this study proposes a multi-stage coordinated robust optimization model for the SOP allocation in ADNs with photovoltaic(PV).First,two robust technical indices based on a robustness index are proposed to evaluate the operation conditions and robust optimality of the solutions.Second,the proposed coordinated allocation model aims to optimize the total cost,robust voltage offset index,robust utilization index,and voltage collapse proximity index.Third,the optimization methods of the multiand single-objective models are coordinated to solve the proposed multi-stage problem.Finally,the proposed model is implemented on an IEEE 33-node distribution system to verify its effectiveness.Numerical results show that the proposed index can better reveal voltage offset conditions as well as the SOP utilization,and the proposed model outperforms conventional ones in terms of robustness of placement plans and total cost.

Distributed cooperative voltage control based on curve-fitting in active distribution networks

This study investigates a hybrid hierarchical multi-agent system for distributed cooperative voltage control in active distribution networks. The hybrid hierarchical multi-agent system adopts on-load tap-changing(OLTC) agents for the distribution transformers and feeder control section(FCS) agents for the distributed generators(DGs). The objective is to minimize the voltage deviations over the network. The FCS agents also have the objective of minimizing reductions in DG power output. A least squares method is used for curve fitting to achieve the two objectives. The OLTC agent receives voltage information from the FCS agents to evaluate the state of the voltage in each feeder and the distribution network and cooperates with the FCS agents to control the voltage of the network.The FCS agents exchange the fitted curve parameters and basic information on the DGs with other agents to achieve the objectives. The effectiveness of the proposed distributed cooperative voltage control scheme is verified through simulations. Depending on the network voltages obtained by the OLTC agent, different operations are executed to prevent voltage limit violations and to minimize the voltage deviations and reductions in the DG power outputs.