Practical Distribution System Research Articles

A Review of Security Evaluation of Practical Quantum Key Distribution System.

Although the unconditional security of quantum key distribution (QKD) has been widely studied, the imperfections of the practical devices leave potential loopholes for Eve to spy the final key. Thus, how to evaluate the security of QKD with realistic devices is always an interesting and opening question. In this paper, we briefly review the development of quantum hacking and security evaluation technology for a practical decoy state BB84 QKD system. The security requirement and parameters in each module (source, encoder, decoder and detector) are discussed, and the relationship between quantum hacking and security parameter are also shown.

Sifting scheme for continuous-variable quantum key distribution with short samples

Preventing the secret key from being stolen is an important issue in practical quantum key distribution systems. In the sifting step, the legitimate parties discard the useless portion of the raw data to form the sifted key. This step is executed at high speed to support the high repetition frequency of the systems without guaranteeing the security of the raw data. In practical systems, useless data contain abnormal data and the key measured by the legitimate party on different bases. Here we propose a sifting scheme based on machine learning that can monitor anomaly quantum signal disturbances in practical continuous-variable quantum key distribution systems. It randomly samples small amounts of data from the data block and uses short samples to preliminarily sift the abnormal one. The results show that the model can quickly distinguish normal communication from most common attacks with the cost of a small part of the raw keys and improve system performance under attacks. In principle, the model can also be generalized and applied to discrete-variable quantum key distribution systems and further enhance the security of quantum key distribution.

Low-carbon distribution system planning considering flexible support of zero-carbon energy station

Zero-carbon energy stations (ZCESs) have a promising prospect in reducing carbon emission, which also results in great impacts on the planning scheme of low-carbon distribution system (DS). In this context, this paper carries out the low-carbon DS planning considering the flexible support of ZCES. Firstly, a low-carbon DS planning model is established, where the material carbon emission and operational carbon emission are both considered. Then, for achieving the low-carbon goal of DS, the flexible support of ZCES is considered during the low-carbon DS planning process. Especially, ZCES is supplied by zero-carbon renewable energy (e.g., photovoltaics and wind power). Meanwhile, DS and ZCES are regarded as different stakeholders, which is addressed by the analytical target cascading (ATC) algorithm. In addition, a distributionally robust optimization method is proposed to cope with the probability distribution (PD) uncertainty of renewable energy and loads. Moreover, a tractable low-carbon planning model for DS considering the flexible support of ZCES is reformulated based on the duality method. Finally, the proposed planning model is tested on a modified IEEE 33-node and a practical 99-node distribution system with ZCES. Numerical results show that the proposed low-carbon planning model is effective in managing PD uncertainties, and improving the low-carbon and economic performance of DS while the ATC algorithm also exhibits good convergence performance.

Bayesian Inference Approach for Information Fusion in Distribution System State Estimation

This paper presents a three-phase Distribution System State Estimator (DSSE) based on a Bayesian inference approach to manage different sampling rates of typical sources of information present in distribution networks. Such information comes from smart meters, supervisory control and data acquisition (SCADA) measurements, phasor measurement units and typical load profiles from pseudo measurements. The temporal aspect of the measurement set is incorporated in the estimation process by using a sampling layer concept, dealing separately with each group of measurements according to the respective updating rate. A Bayesian information fusion procedure provides the final estimation. The proposed DSSE consists in a multiple stage estimator that combines a prior model for the state variables, updated by new observations from measured values in each sampling layer, through Maximum a Posteriori estimation. This work also introduces an orthogonal method for the information fusion numerical solution, to tackle the severe ill-conditioning associated with practical distribution systems. Simulations with IEEE distribution test feeders and a Brazilian real distribution feeder illustrate the features of the proposed DSSE and its applicability. By exploring the concept of credibility intervals, the method is able to detect events on the grid, such as subtle load variation and contingencies, while maintaining accuracy.

Renewable energy source allocation in electrical distribution system using water cycle algorithm

Achieving maximum potential benefits through appropriate allocation of distributed generation (DG) in electrical distribution systems is yet a challenging issue. Since the inappropriate allocation of DG adversely affects the distribution system. The present paper introduces a novel metaheuristic optimization method for solving the allocation of renewable energy sources (RES) in a practical distribution system. The algorithm has been effectively implemented on a practical Indian 28 – bus distribution system to allocate wind turbines (WT) and solar photovoltaic (SPV) arrays to achieve technical benefits of power loss minimization and bus voltage profile improvement. The simulation results obtained by the present method are compared with existing literature and it is found to be better.

State preparation error tolerant quantum key distribution protocol based on heralded single photon source

In practical quantum key distribution systems, there inevitably exist errors in the quantum state preparation process due to imperfections in realistic equipment and devices. Those errors would lead to some security loopholes in the quantum key distribution systems. According to the work of Tamaki et al. (<ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="http://doi.org/10.1103/PhysRevA.90.0523142014"><i>Phys. Rev. A</i> <b>90</b> 052314</ext-link>), here in this work we propose a state preparation error tolerant quantum key distribution protocol through using heralded single-photon sources. In this protocol, we characterize the size of the error in the preparation state of Alice and bring it into the security analysis, thereby avoiding possible security loopholes and improving the security of the system. Moreover, we take the three-intensity decoy-state method for example to introduce the method of constructing the model and estimating the parameters, and carry out corresponding numerical simulations. We make a comparison between the loss tolerant protocol with weak coherent source (WCS) and our present protocol using heralded single-photon source (HSPS). Simulation results show that under the same state preparation error, the key generation rate of the protocol based on WCS is higher than that of protocol based on HSPS at short transmission distances (e.g. less than 150 km). The main reason is that the detection efficiency of the local detector used in the latter scheme is low. However, in the case of long transmission distances (e.g. greater than 200 km), the key generation rate of scheme with WCS drops deeply, while the decline of the key generation rate of the present scheme is much flatter. As a result, the former can no longer generate keys after 211 km, while the latter can transmit a maximum distance of 228 km. Moreover, we also make a comparison between the present scheme and the GLLP protocol with HSPS. The simulation results show that the GLLP protocol with HSPS is very sensitive to the state preparation error and its key generation rate will rapidly decrease with the increase of the state preparation error. On the contrary, our present protocol shows almost no performance degradation under practical state preparation errors. It thus verify the robustness against the state preparation errors of our present work. In addition, in principle, the method can also be combined with the measurement-device-independent quantum key distribution protocol and the twin-field quantum key distribution protocol to further increase the secure communication transmission distance that the present system can reach. Therefore, this work may provide an important reference value for the practical application of long-distance quantum secure communication in the near future.

Generalized Nash equilibrium analysis of transmission and distribution coordination in coexistence of centralized and local markets

The increasing scale of distributed energy resources connected to distribution networks can not only solve the operational problems in the distribution system, but it can also supply the transmission system with flexibility and ancillary services. Thus, the design of a market framework to suit this situation is required. This paper builds a generalized Nash game model with shared constraints for the joint day-ahead market where the centralized market coexists with the local market. In this model, the transmission system operator clears the centralized market assuming that resources of distribution systems are fixed, the distribution system operator clears the local market assuming that resources of the transmission system are fixed, and the clearing of the two markets are conducted simultaneously. Subsequently, the generalized Nash game model can be reformulated to a variational inequality problem that can be solved by the Goldstein–Levitin–Polyak projection method, and more than one generalized Nash equilibrium solution is obtained by the parametrized variational inequality approach. Finally, case studies on two systems, which consist of a 30-bus transmission system and 33-bus distribution system, and a practical 343-bus transmission system and 110-bus distribution system, respectively, are used to verify the effectiveness of the proposed method.

Estimating the Photon-Number Distribution of Photonic Channels for Realistic Devices and Applications in Photonic Quantum Information Processing

Characterising the input-output photon-number distribution of an unknown optical quantum channel is an important task for many applications in quantum information processing. Ideally, this would require deterministic photon-number sources and photon-number-resolving detectors, but these technologies are still work-in-progress. In this work, we propose a general method to rigorously bound the input-output photon number distribution of an unknown optical channel using standard optical devices such as coherent light sources and non-photon-number-resolving detectors/homodyne detectors. To demonstrate the broad utility of our method, we consider the security analysis of practical quantum key distribution systems based on calibrated single-photon detectors and an experimental proposal to implement time-correlated single photon counting technology using homodyne detectors instead of single-photon detectors.

Research on Game Operation of Multi-Stakeholder in Distribution Network in Electricity Market

With the development of the electricity market, various stakeholders such as batteries, multi-microgrid (MMG), and electric vehicle (EV) clusters, can trade with either the distribution network or each other to meet their power balance needs and to maximize their profits. This paper proposes a two-level game model based on game theory to study the operation strategy of stakeholders in the distribution network. First, each stakeholder predicts its electricity demand profile. A Markov Decision Process (MDP) model of random variables is established to predict the charging and discharging power of the battery. Then, the two-level game is presented to let multi-stakeholder participate, in which different kinds of stakeholders have different game strategy limits. Additionally, suggestions for battery operation modes under different compensation coefficients are given to participate in the subsequent two-level game. An algorithm is proposed to allow stakeholders to merge or split self-adaptively based on Nondominated Sorting Genetic Algorithm II (NSGA-II) to optimize operation mode. Finally, the proposed model is applied to the PG and E69-bus distribution system and a practical 101-bus distribution system in China. The case studies show that different game strategy limits of the stakeholders will affect the distribution of the Nash equilibrium (NE) solutions. The multi-stakeholder system can better absorb regional unbalanced power through electricity transactions, and further increase the benefits of each stakeholder.

A Novel Faulty Phase Selection Method for Single-Phase-to-Ground Fault in Distribution System Based on Transient Current Similarity Measurement

In modern electrical power distribution systems, the effective operation of inverter-based arc suppression devices relies on the accuracy of faulty phase selection. In the traditional methods of faulty phase selection for single-phase-to-ground faults (SPGs), power frequency-based amplitude and phase characteristics are used to identify the faulty phase. In the field, when a high-resistance SPG occurs in the system, traditional methods are difficult for accurately identifying the faulty phase because of the weak fault components and complicated process. A novel realizable and effective method of faulty phase selection based on transient current similarity measurements is presented when SPGs occur in resonantly grounded distribution systems in this paper. An optimized Hausdorff distance matrix (MOHD) is proposed and constructed by the transient currents of three phases’ similarity measurements within a certain time window of our method. This MOHD is used to select the sampling time window adaptively, which allows the proposed method to be applied to any scale of distribution systems. Firstly, when a SPG occurs, the expressions for the transient phase current mutation in the faulty and sound phases are analyzed. Then, the sampling process is segmented into several selection units (SUs) to form the MOHD-based faulty phase selection method. Additionally, the Hausdorff distance algorithm (HD) is used to calculate the waveform similarities of the transient phase current mutation among the three phases to form the HD-based faulty phase selection method. Finally, a practical resonant grounded distribution system is modeled in PSCAD/EMTDC, and the effectiveness and performance of the proposed method is compared and verified under different fault resistances, fault inception angles, system topologies, sampling time windows and rates of data missing.

Characterization and assessment of organic carbon migration and biomass formation potential of polymeric pipes using modified BioMig

To evaluate and approve the materials that can be in contact with drinking water with respect to biological contamination, organic carbon migration and biomass formation potential assays are widely employed. In previous studies, to conduct the assays, the entire material was exposed to the test water by submerging the material into glass jars. However, these are not realistic conditions since the water contact only the inner surface of the pipe in the distribution system, which may be different from the outer surface with respect to the coating layers. Furthermore, the extra components such as the glass jar containers and stainless steel holders might be the sources of organic carbon contamination. These two factors may result in imprecise conclusions. Here, we introduced a minor but essential modification to the BioMig test, which is the most current test on the migration potential and biomass formation potential. In this modified BioMig method, the outer surface and cutting edges, which are not in contact with water in practical water distribution systems, would not be exposed to the test water. Moreover, the assimilable organic carbon formation potential via chlorination in the leachates was observed and recommended as an additional test in the migration potential assay. The modified test results demonstrated that polyethylene (PE) material is the most unfavorable material, notably when used in chlorinated drinking water. This proposed test provided an alternative approach to the decision-makers with respect to evaluating the pipe material characteristics.

Bi-level programming approach for coordinated configuration of distributed generations and automation devices in distribution networks

Improving system reliability and voltage profiles in distribution networks requires the simultaneous installation of distributed generations and automation devices. This paper builds a bi-level programming model for the coordinated configuration of distributed generations and automation devices. The upper-level problem is to minimize the total investment cost of distributed generations and automation devices and the penalty cost of power shortages, and the lower-level problem is to minimize the voltage deviation during typical operation mode. To explicitly represent the relationship between the penalty cost of power shortages and installation locations of distributed generations and automation devices, an analytical expression of the failure rates of load nodes with respect to installation locations of distributed generations and automation devices is derived. The bi-level model can be transformed to a mixed-integer nonlinear programming problem by applying the Karush–Kuhn–Tucker theorem because the lower-level problem is a quadratically constrained linear programming problem. Case studies on an IEEE 33-bus distribution system and a practical 110-bus distribution system in China are used to verify the effectiveness of the proposed method.

A time of use tariff scheme for demand side management of residential energy consumers in Bangladesh

The aim of this paper is to develop a new Time of Use (TOU) tariff scheme for Demand Side Management (DSM) of residential consumers in Bangladesh. In this advanced era of technology, the requirement of electrical energy is continuously increasing. However, the overall growth of electricity generation is comparatively slower due to depletion of fuel sources. In this situation, a strategic plan needs to be implemented at consumer end to mitigate the energy crisis. As such, the concept of DSM is introduced where consumers can save electricity bills by changing their consumption pattern. It subsequently reduces the generation requirement during peak hours, which offers operational flexibility and financial benefits to utility. In the literature, DSM has been used in residential, commercial, and industrial sectors. However, the existing techniques do not explicitly consider the low-income consumers when TOU scheme is utilised. It may result in financial repercussion for these consumers. To overcome this challenge, a TOU scheme is proposed in this paper by ensuring financial benefits for all types of residential consumers and utility. To this end, an optimisation model is formulated by taking into account the various consumer groups such as low-income, middle-income and high-income. Two different meters (meter 1 and meter 2) are prudently allocated to these consumers. Using genetic algorithm technique, optimisation model is solved to provide electricity tariff and block sizes for various consumers under two meters. The proposed algorithm is applied to the residential inhabitants of a practical distribution system in Bangladesh. The results suggest that significant amount of annual savings can be achieved simultaneously by consumers and utility. The low-income consumers remain financially least affected while the proposed TOU tariff scheme is executed. In addition, the proposed TOU approach is compared to existing methods and found that optimisation based TOU outperforms the conventional ones.

A two-stage approach to enhance distribution network resilience against natural disasters

The natural disasters such as floods and storms have always led to widespread damage and seriously disrupt at the power distribution network. This paper, after presenting a new index to quantitatively calculate the resilience of the distribution network, proposes a two-stage approach to enhance distribution network resilience against natural disasters in the presence of distributed generation (DG) including photovoltaic and conventional gas-fired power sources. The first stage includes determining the boundaries of isolated zones and optimal capacity of the DG units in each zone with the aim of improving the resilience by considering budget constraint. The second stage includes determining the optimal location of DG in each zone with the aim of optimizing the distribution network losses. A genetic algorithm (GA) based optimization method is developed to solve the proposed problem. The performance of the proposed approach is assessed and illustrated by numerical studies on a practical distribution system from Iran.

Rate compatible reconciliation for continuous-variable quantum key distribution using Raptor-like LDPC codes

In the practical continuous-variable quantum key distribution (CV-QKD) system, the postprocessing process, particularly the error correction part, significantly impacts the system performance. Multi-edge type low-density parity-check (MET-LDPC) codes are suitable for CV-QKD systems because of their Shannon-limit-approaching performance at a low signal-to-noise ratio (SNR). However, the process of designing a low-rate MET-LDPC code with good performance is extremely complicated. Thus, we introduce Raptor-like LDPC (RL-LDPC) codes into the CV-QKD system, exhibiting both the rate compatible property of the Raptor code and capacity-approaching performance of MET-LDPC codes. Moreover, this technique can significantly reduce the cost of constructing a new matrix. We design the RL-LDPC matrix with a code rate of 0.02 and easily and effectively adjust this rate from 0.016 to 0.034. Simulation results show that we can achieve more than 98% reconciliation efficiency in a range of code rate variation using only one RL-LDPC code that can support high-speed decoding with an SNR less than -16.45 dB. This code allows the system to maintain a high key extraction rate under various SNRs, paving the way for practical applications of CV-QKD systems with different transmission distances.

Scenario-Based Stochastic Framework for Optimal Planning of Distribution Systems Including Renewable-Based DG Units

Renewable energy-based distributed generators are widely embedded into distribution systems for several economical, technical, and environmental tasks. The main concern related to the renewable-based distributed generators, especially photovoltaic and wind turbine generators, is the continuous variations in their output powers due to variations in solar irradiance and wind speed, which leads to uncertainties in the power system. Therefore, the uncertainties of these resources should be considered for feasible planning. The main innovation of this paper is that it proposes an efficient stochastic framework for the optimal planning of distribution systems with optimal inclusion of renewable-based distributed generators, considering the uncertainties of load demands and the output powers of the distributed generators. The proposed stochastic framework depends upon the scenario-based method for modeling the uncertainties in distribution systems. In this framework, a multi-objective function is considered for optimal planning, including minimization of the expected total power loss, the total system voltage deviation, the total cost, and the total emissions, in addition to enhancing the expected total voltage stability. A novel efficient technique known as the Equilibrium Optimizer (EO) is actualized to appoint the ratings and locations of renewable-based distributed generators. The effectiveness of the proposed strategy is applied on an IEEE 69-bus network and a 94-bus practical distribution system situated in Portugal. The simulations verify the feasibility of the framework for optimal power planning. Additionally, the results show that the optimal integration of the photovoltaic and wind turbine generators using the proposed method leads to a reduction in the expected power losses, voltage deviations, cost, and emission rate and enhances the voltage stability by 60.95%, 37.09%, 2.91%, 70.66%, and 48.73%, respectively, in the 69-bus system, while in the 94-bus system these values are enhanced to be 48.38%, 39.73%, 57.06%, 76.42%, and 11.99%, respectively.

High-Accuracy Power Quality Disturbance Classification Using the Adaptive ABC-PSO as Optimal Feature Selection Algorithm

Power quality disturbance (PQD) is an important issue in electrical distribution systems that needs to be detected promptly and identified to prevent the degradation of system reliability. This work proposes a PQD classification using a novel algorithm, comprised of the artificial bee colony (ABC) and the particle swarm optimization (PSO) algorithms, called “adaptive ABC-PSO” as the feature selection algorithm. The proposed adaptive technique is applied to a combination of ABC and PSO algorithms, and then used as the feature selection algorithm. A discrete wavelet transform is used as the feature extraction method, and a probabilistic neural network is used as the classifier. We found that the highest classification accuracy (99.31%) could be achieved through nine optimally selected features out of all 72 extracted features. Moreover, the proposed PQD classification system demonstrated high performance in a noisy environment, as well as the real distribution system. When comparing the presented PQD classification system’s performance to previous studies, PQD classification accuracy using adaptive ABC-PSO as the optimal feature selection algorithm is considered to be at a high-range scale; therefore, the adaptive ABC-PSO algorithm can be used to classify the PQD in a practical electrical distribution system.

Quantum Hacking on an Integrated Continuous-Variable Quantum Key Distribution System via Power Analysis.

In quantum key distribution (QKD), there are some security loopholes opened by the gaps between the theoretical model and the practical system, and they may be exploited by eavesdroppers (Eve) to obtain secret key information without being detected. This is an effective quantum hacking strategy that seriously threatens the security of practical QKD systems. In this paper, we propose a new quantum hacking attack on an integrated silicon photonic continuous-variable quantum key distribution (CVQKD) system, which is known as a power analysis attack. This attack can be implemented by analyzing the power originating from the integrated electrical control circuit in state preparation with the help of machine learning, where the state preparation is assumed to be perfect in initial security proofs. Specifically, we describe a possible power model and show a complete attack based on a support vector regression (SVR) algorithm. The simulation results show that the secret key information decreases with the increase of the accuracy of the attack, especially in a situation with less excess noise. In particular, Eve does not have to intrude into the transmitter chip (Alice), and may perform a similar attack in practical chip-based discrete-variable quantum key distribution (DVQKD) systems. To resist this attack, the electrical control circuit should be improved to randomize the corresponding power. In addition, the power can be reduced by utilizing the dynamic voltage and frequency scaling (DVFS) technology.

Designing optimal route for the distribution chain of a rural LPG delivery system

A practical distribution system that arises in the context of delivering liquefied petroleum gas (LPG) through cylinders is considered in this study. To meet all the challenging constraints, the model is explicitly considered as a simultaneous pickup and delivery single commodity truncated vehicle routing problem with the homogeneous fleet of vehicles. The aim of this problem is to find the optimal routes for the set of vehicles locating at the distributing agency (DA), which offers simultaneous pickup and delivery operations over single commodity (i.e. LPG cylinders) to a fixed subset (need not serve all delivery centers) of delivery centers at rural level. The model is designed using zero-one integer linear programming. For proper treatment of the present model, an exact Lexi-search algorithm (LSA) has been developed. A comparative study is performed between the LSA and existing results for the relaxed version of the present model. Further, the efficiency of the LSA is tested through numerical experiments over small and medium CVRP benchmark test instances. The extensive computational results have shown that the LSA is productive and revealed that the real solutions have more consistent than the integral solutions in the presence of truncation constraint.

A Two-stage Robust Optimal Allocation Model of Distributed Generation Considering Capacity Curve and Real-time Price Based Demand Response

Demand response, the reactive power output of distributed generation (DG), and network reconfiguration have significant impacts on a DG allocation strategy. In this context, a novel real-time price-based demand response formulation is integrated into the allocation model of DG. The tariff is regulated by the difference between the load and active power of renewable energy. Meanwhile, network reconfiguration and the capacity curve describing the active and reactive power limits of DG are included in the optimization model for promoting the allocation of DG. With these measures, the optimal allocation model of DG is established with the goal of maximizing the net annual profit while guaranteeing the efficient utilization of renewable energy. In addition, the uncertainties of renewable energy are considered on the basis of a two-stage robust optimization method. Finally, the entire optimization model is solved by the column and constraint generation algorithm in the IEEE 33-bus distribution system and a practical 99-bus distribution system. Numerical simulations show that the proposed model is effective in terms of improving both the usage of renewable energy and net annual profit.