{"title":"Two probabilistic methods for voltage sag estimation in distribution systems","authors":"J. Baptista, A. Rodrigues, Maria G. Silva","doi":"10.1109/PSCC.2016.7540905","DOIUrl":"https://doi.org/10.1109/PSCC.2016.7540905","url":null,"abstract":"This paper presents a comparison between Monte Carlo simulation and fault position (state enumeration) methods applied for estimating voltage sag indices in radial unbalanced distribution network regarding to accuracy and CPU time. The system is represented in phase coordinates and the fault analysis is fulfilled using the admittance summation method. Tests are performed in the medium voltage CIGRÈ Benchmark System for Network Integration of Renewable and Distributed Energy Resources. The results show that the fault position method is competitive with the Monte Carlo simulation regarding to the precision of the assessed indices even when a few states are considered. It results in a smaller CPU time for the fault position in comparison with the Monte Carlo method.","PeriodicalId":265395,"journal":{"name":"2016 Power Systems Computation Conference (PSCC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125332382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
C. J. Lopez-Salgado, O. Añó, Diego M. Ojeda-Esteybar, Fabricio Porras
{"title":"Joint optimization of energy and reserve in deregulated power markets: Alternative approach using Mean Variance Mapping Optimization","authors":"C. J. Lopez-Salgado, O. Añó, Diego M. Ojeda-Esteybar, Fabricio Porras","doi":"10.1109/PSCC.2016.7540934","DOIUrl":"https://doi.org/10.1109/PSCC.2016.7540934","url":null,"abstract":"This paper presents an optimization methodology to perform simultaneous energy and reserve scheduling, considering the transmission network and forced outages of generating units and transmission lines. The strategy is structured by linking a set of mathematical programming tools to the Mean Variance Mapping Optimization algorithm, an emergent evolutionary strategy. A distinctive feature of the proposal is the election of required nodal reserve as a decision variable in the meta heuristic algorithm, which improves the speed at which the method approaches an optimal configuration. The cost of contingencies during the reserve deployment stage is considered in the formulation. Results indicate that a near optimal cost is reached with much less computational effort than that exhibited by existing proposals. An example is presented to illustrate and test the proposed scheme.","PeriodicalId":265395,"journal":{"name":"2016 Power Systems Computation Conference (PSCC)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126677878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Chanaka Keerthisinghe, G. Verbič, Archie C. Chapman
{"title":"Energy management of PV-storage systems: ADP approach with temporal difference learning","authors":"Chanaka Keerthisinghe, G. Verbič, Archie C. Chapman","doi":"10.1109/PSCC.2016.7540924","DOIUrl":"https://doi.org/10.1109/PSCC.2016.7540924","url":null,"abstract":"In the future, residential energy users can seize the full potential of demand response schemes by using an automated home energy management system (HEMS) to schedule their distributed energy resources. In order to generate high quality schedules, a HEMS needs to consider the stochastic nature of the PV generation and energy consumption as well as its inter-daily variations over several days. However, extending the decision horizon of proposed optimisation techniques is computationally difficult and moreover, these approaches are only computationally feasible with a limited number of storage devices and a low-resolution decision horizon. Given these existing shortcomings, this paper presents an approximate dynamic programming (ADP) approach with temporal difference learning for implementing a computationally efficient HEMS. In ADP, we obtain policies from value function approximations by stepping forward in time, compared to the value functions obtained by backward induction in DP. We use empirical data collected during the Smart Grid Smart City project in NSW, Australia, to estimate the parameters of a Markov chain model of PV output and electrical demand, which are then used in all simulations. To evaluate the quality of the solutions generated by ADP, we compare the ADP method to stochastic mixed-integer linear programming (MILP) and dynamic programming (DP). Our results show that ADP computes a solution much quicker than both DP and stochastic MILP, while providing better quality solutions than stochastic MILP and only a slight reduction in quality compared to the DP solution. Moreover, unlike the computationally-intensive DP, the ADP approach is able to consider a decision horizon beyond one day while also considering multiple storage devices, which results in a HEMS that can capture additional financial benefits","PeriodicalId":265395,"journal":{"name":"2016 Power Systems Computation Conference (PSCC)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124141492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Quentin Gemine, B. Cornélusse, M. Glavic, R. Fonteneau, D. Ernst
{"title":"A Gaussian mixture approach to model stochastic processes in power systems","authors":"Quentin Gemine, B. Cornélusse, M. Glavic, R. Fonteneau, D. Ernst","doi":"10.1109/PSCC.2016.7540921","DOIUrl":"https://doi.org/10.1109/PSCC.2016.7540921","url":null,"abstract":"Probabilistic methods are emerging for operating electrical networks, driven by the integration of renewable generation. We present an algorithm that models a stochastic process as a Markov process using a multivariate Gaussian Mixture Model, as well as a model selection technique to search for the adequate Markov order and number of components. The main motivation is to sample future trajectories of these processes from their last available observations (i.e. measurements). An accurate model that can generate these synthetic trajectories is critical for applications such as security analysis or decision making based on lookahead models. The proposed approach is evaluated in a lookahead security analysis framework, i.e. by estimating the probability of future system states to respect operational constraints. The evaluation is performed using a 33-bus distribution test system, for power consumption and wind speed processes. Empirical results show that the GMM approach slightly outperforms an ARMA approach.","PeriodicalId":265395,"journal":{"name":"2016 Power Systems Computation Conference (PSCC)","volume":"143 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122667457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Optimal allocation of smart metering systems for enhanced distribution system state estimation","authors":"T. Xygkis, G. Korres","doi":"10.1109/PSCC.2016.7540966","DOIUrl":"https://doi.org/10.1109/PSCC.2016.7540966","url":null,"abstract":"Smart meters are the backbone of advanced metering systems for accurate billing, load estimation and demand side response in smart distribution grids. By appropriately selecting their installation locations, it is feasible to achieve more precise distribution state estimation. This paper presents a semidefinite programming method to optimally allocate smart metering systems in distribution grids in order to minimize the state estimation error variances. The placement problem is formulated as an M-optimal experimental design model and is transformed into a binary semidefinite programming model, with binary decision variables, minimizing a linear objective function subject to linear matrix inequality constraints. Simulations using a 55-bus distribution grid are carried out in order to verify the optimality of the chosen meter locations in terms of minimizing bus voltage magnitude and angle variances.","PeriodicalId":265395,"journal":{"name":"2016 Power Systems Computation Conference (PSCC)","volume":"334 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123315064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Mitigation methods to improve the lightning performance of hybrid transmission line","authors":"A. Mackow, M. Kizilcay","doi":"10.1109/PSCC.2016.7540969","DOIUrl":"https://doi.org/10.1109/PSCC.2016.7540969","url":null,"abstract":"EMTP simulations and lightning attachment models are performed to estimate backflashover performance of multi-circuit transmission tower. Multi-circuit transmission tower has several systems on the tower and combines DC transmission over long distances with more flexible AC transmission. The outcome of simulations and calculations should give the range of backflashover withstand current and backflashover outage level. Maximum lightning current amplitude that does not cause backflashover across insulator string is estimated in response to first and subsequent strokes several flashover models. The paper presents a new unbalanced design of insulation. Latter should decrease backflashover rate for lines serving critical loads like HVDC lines.","PeriodicalId":265395,"journal":{"name":"2016 Power Systems Computation Conference (PSCC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122316983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Reliability improvement of Modular Multilevel Converter in HVDC systems","authors":"Stefano Farnesi, M. Marchesoni, L. Vaccaro","doi":"10.1109/PSCC.2016.7540942","DOIUrl":"https://doi.org/10.1109/PSCC.2016.7540942","url":null,"abstract":"In this paper the reliability of a Modular Multilevel Converter for HVDC applications is incremented, by decreasing the electrical stress of the main passive component suitable to fault, that is, the capacitor cell. This effect is obtained with the injection of a suitable cell second harmonic circulating current. It's important to note how the cell current increment does not increase the semiconductor losses, but in some circumstances can even decrease such losses.","PeriodicalId":265395,"journal":{"name":"2016 Power Systems Computation Conference (PSCC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129579159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A coordinated cyber attack detection system (CCADS) for multiple substations","authors":"Chih-Che Sun, Junho Hong, Chen-Ching Liu","doi":"10.1109/PSCC.2016.7540902","DOIUrl":"https://doi.org/10.1109/PSCC.2016.7540902","url":null,"abstract":"In recent years, the concern over cyber security of power grids has increased significantly due to the fast growing connectivity among power system facilities. Several cyber security measures, e.g., intrusion detection systems (IDSs) and anomaly detection systems (ADSs), have been proposed to (1) mitigate unauthorized access, (2) detect anomalies, and (3) block abnormal behaviors in the communication system of substations. However, due to the lack of capability to handle coordinated cyber attacks by existing cyber security solutions, there is a need for effective methods that can detect coordinated cyber attacks. This paper proposes a new method to detect coordinated cyber attacks on power systems by identifying the relations among detected events. Examples of the relations include (1) IDS alarms, (2) geographic location of the attack, (3) criticality of substations, (4) firewall logs, and (5) attack patterns. Time Failure Propagation Graph (TFPG) and Fuzzy Cognitive Map (FCM) are used for the detection algorithms. A cyber-physical security testbed has been used to simulate the coordinated cyber attacks and validate the methods of the proposed coordinated cyber attack detection system (CCADS).","PeriodicalId":265395,"journal":{"name":"2016 Power Systems Computation Conference (PSCC)","volume":"189 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134011507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Validation of the Ornstein-Uhlenbeck process for load modeling based on µPMU measurements","authors":"C. Roberts, E. Stewart, F. Milano","doi":"10.1109/PSCC.2016.7540898","DOIUrl":"https://doi.org/10.1109/PSCC.2016.7540898","url":null,"abstract":"This paper investigates the suitability of the Ornstein-Uhlenbeck process, driven by various Lévy processes, for load modeling at the distribution network level. An indepth description outlining the procedure for estimating the required parameters is given. Both the statistical properties of the simulated processes and its auto-correlation is compared to that of the field measured data to demonstrate the suitability of the proposed methodology. The development of such stochastic models is facilitated by measures obtained from micro-synchrophasors (μPMU's). The data from these devices serves to demonstrate the need to model the volatility along with validating a model attempting to model said volatility.","PeriodicalId":265395,"journal":{"name":"2016 Power Systems Computation Conference (PSCC)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132777010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Sauhats, R. Petrichenko, K. Baltputnis, Z. Broka, R. Varfolomejeva
{"title":"A multi-objective stochastic approach to hydroelectric power generation scheduling","authors":"A. Sauhats, R. Petrichenko, K. Baltputnis, Z. Broka, R. Varfolomejeva","doi":"10.1109/PSCC.2016.7540821","DOIUrl":"https://doi.org/10.1109/PSCC.2016.7540821","url":null,"abstract":"In this paper, we propose a novel stochastic approach to multi-objective optimization of hydroelectric power generation short-term scheduling. Maximization of profit is chosen as the main objective with additional sub-objective-to reduce the number of startups and shutdowns of generating units. The random nature of future electricity prices and river water inflow is taken into account. We use an artificial neural network-based algorithm to forecast market prices and water inflow. Uncertainty modeling is introduced to represent the stochastic nature of parameters and to solve the short-term optimization problem of profit-based unit commitment. A case study is conducted on a real-world hydropower plant to demonstrate the feasibility of the proposed algorithm by providing the power generation company with the day-ahead bidding strategy under market conditions and a Pareto optimal hourly dispatch schedule of the generating units.","PeriodicalId":265395,"journal":{"name":"2016 Power Systems Computation Conference (PSCC)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132971462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}