Pub Date : 2018-10-01DOI: 10.1109/SmartGridComm.2018.8587482
F. Schäfer, J. Menke, M. Braun
A fast assessment of the single contingency policy for power systems is crucial in power system planning and live operation. Power system planning methods based on thousands of power flow calculations, such as time series based grid planning strategies, rely on a fast evaluation of loadings in case of simulated outages. Standard approximation methods, such as the line outage distribution factor (LODF) matrix, have limited accuracy and can only approximate real power flows. To increase accuracy and to predict other power system parameters, we perform contingency analysis with artificial neural networks. Deep feedforward network architectures are trained with 20% of AC power flow results from time series simulation of one year. The remaining line loadings and bus voltages are then predicted. Detailed analyses are conducted on a real German 110 kV sub-transmission grid located in Karlsruhe. The method is additionally tested on the IEEE57 bus system and the CIGRE15 bus medium voltage grid. For each test grid prediction errors are extremely low (0.5%) in comparison to the LODF method (18.6%). Prediction times are significantly less compared to AC power flow calculations (10s vs. 1861s).
{"title":"Contingency Analysis of Power Systems with Artificial Neural Networks","authors":"F. Schäfer, J. Menke, M. Braun","doi":"10.1109/SmartGridComm.2018.8587482","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2018.8587482","url":null,"abstract":"A fast assessment of the single contingency policy for power systems is crucial in power system planning and live operation. Power system planning methods based on thousands of power flow calculations, such as time series based grid planning strategies, rely on a fast evaluation of loadings in case of simulated outages. Standard approximation methods, such as the line outage distribution factor (LODF) matrix, have limited accuracy and can only approximate real power flows. To increase accuracy and to predict other power system parameters, we perform contingency analysis with artificial neural networks. Deep feedforward network architectures are trained with 20% of AC power flow results from time series simulation of one year. The remaining line loadings and bus voltages are then predicted. Detailed analyses are conducted on a real German 110 kV sub-transmission grid located in Karlsruhe. The method is additionally tested on the IEEE57 bus system and the CIGRE15 bus medium voltage grid. For each test grid prediction errors are extremely low (0.5%) in comparison to the LODF method (18.6%). Prediction times are significantly less compared to AC power flow calculations (10s vs. 1861s).","PeriodicalId":213523,"journal":{"name":"2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134569705","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}
Pub Date : 2018-10-01DOI: 10.1109/SmartGridComm.2018.8587590
Thanasis G. Papaioannou, G. Stamoulis, Marilena Minou
Automated Demand Response (ADR) can facilitate residential customers to effectively reduce their energy demand and make savings in a simple way, provided that appropriate incentives are offered to them. Most often, incentives involved in ADR contracts are statically defined and assume full customer rationality, thus hindering sustained customer enrollment to them of customers with other characteristics (e.g. altruism). In this paper, we derive appropriate (and personalized) incentives for ADR contracts, so that non-fully rational customers are compensated even when information for consumer utilities is not available. In case such information is hidden, we assume that customers provide feedback on their satisfaction from direct endowments, albeit sustaining energy-consumption reduction. Moreover, we consider the case where customers may strategically lie on their satisfaction from ADR incentives, so as to self-optimize. We mathematically model the customer and the utility company’s problems and solve them algebraically or in a distributed manner. Furthermore, based on customer feedback on appropriate endowments for different energy-consumption reductions, we propose an algorithm that can find the optimal set of satisfied targeted customers, which achieve the total desired energy-consumption reduction at the minimum endowment cost. Based on numerical evaluation and simulation experiments, we showcase the validity of our analytical framework in realistic scenarios and that, for the case of hidden information, customer feedback is adequate for calculating incentives that can lead to successful DR campaigns.
{"title":"Personalized Feedback-based Customer Incentives in Automated Demand Response","authors":"Thanasis G. Papaioannou, G. Stamoulis, Marilena Minou","doi":"10.1109/SmartGridComm.2018.8587590","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2018.8587590","url":null,"abstract":"Automated Demand Response (ADR) can facilitate residential customers to effectively reduce their energy demand and make savings in a simple way, provided that appropriate incentives are offered to them. Most often, incentives involved in ADR contracts are statically defined and assume full customer rationality, thus hindering sustained customer enrollment to them of customers with other characteristics (e.g. altruism). In this paper, we derive appropriate (and personalized) incentives for ADR contracts, so that non-fully rational customers are compensated even when information for consumer utilities is not available. In case such information is hidden, we assume that customers provide feedback on their satisfaction from direct endowments, albeit sustaining energy-consumption reduction. Moreover, we consider the case where customers may strategically lie on their satisfaction from ADR incentives, so as to self-optimize. We mathematically model the customer and the utility company’s problems and solve them algebraically or in a distributed manner. Furthermore, based on customer feedback on appropriate endowments for different energy-consumption reductions, we propose an algorithm that can find the optimal set of satisfied targeted customers, which achieve the total desired energy-consumption reduction at the minimum endowment cost. Based on numerical evaluation and simulation experiments, we showcase the validity of our analytical framework in realistic scenarios and that, for the case of hidden information, customer feedback is adequate for calculating incentives that can lead to successful DR campaigns.","PeriodicalId":213523,"journal":{"name":"2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115533870","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}
Pub Date : 2018-10-01DOI: 10.1109/SmartGridComm.2018.8587497
Majid Khonji, S. Chau, Khaled M. Elbassioni
This paper studies the scheduling optimization problem of electric vehicle (EV) charging considering two salient characteristics: (1) discrete charging rates with minimum power requirements in common EV charging standards, and (2) nodal voltage and line capacity constraints of alternating current (AC) power flows in electricity distribution networks. We present approximation algorithms to solve scheduling optimization problem of EV charging, which have a provably small parameterized approximation ratio. Simulations show our algorithms can produce close-to-optimal solutions in practice.
{"title":"Combinatorial Optimization of Electric Vehicle Charging in AC Power Distribution Networks","authors":"Majid Khonji, S. Chau, Khaled M. Elbassioni","doi":"10.1109/SmartGridComm.2018.8587497","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2018.8587497","url":null,"abstract":"This paper studies the scheduling optimization problem of electric vehicle (EV) charging considering two salient characteristics: (1) discrete charging rates with minimum power requirements in common EV charging standards, and (2) nodal voltage and line capacity constraints of alternating current (AC) power flows in electricity distribution networks. We present approximation algorithms to solve scheduling optimization problem of EV charging, which have a provably small parameterized approximation ratio. Simulations show our algorithms can produce close-to-optimal solutions in practice.","PeriodicalId":213523,"journal":{"name":"2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116377047","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}
Pub Date : 2018-10-01DOI: 10.1109/SmartGridComm.2018.8587478
Mohammed S. Kemal, R. Olsen, H. Schwefel
Smart meter data is usually accessed periodically with low time granularity, which creates limitations for near real-time information. This paper first introduces information quality metrics that can be used to optimize real-time data access to smart meter data. Then, a systematic parametric study assesses the impact of smart meter access scheduling on information quality. Finally, the paper evaluates a previously proposed heuristic optimization of scheduling of smart meter data access. The result shows that the heuristic optimization algorithm in all investigated scenarios shows less than 15% degradation as compared to the achievable best schedule.
{"title":"Optimized Scheduling of Smart Meter Data Access: A Parametric Study","authors":"Mohammed S. Kemal, R. Olsen, H. Schwefel","doi":"10.1109/SmartGridComm.2018.8587478","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2018.8587478","url":null,"abstract":"Smart meter data is usually accessed periodically with low time granularity, which creates limitations for near real-time information. This paper first introduces information quality metrics that can be used to optimize real-time data access to smart meter data. Then, a systematic parametric study assesses the impact of smart meter access scheduling on information quality. Finally, the paper evaluates a previously proposed heuristic optimization of scheduling of smart meter data access. The result shows that the heuristic optimization algorithm in all investigated scenarios shows less than 15% degradation as compared to the achievable best schedule.","PeriodicalId":213523,"journal":{"name":"2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129207233","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}
Pub Date : 2018-10-01DOI: 10.1109/SmartGridComm.2018.8587507
M. Mallick, Pirathayini Srikantha
The recent amalgamation of advanced communication and actuation capabilities into power entities is fundamental for enabling the design of an adaptive, efficient and resilient power grid. In this paper, we focus specifically on optimal and decentralized microgrid coordination that accounts for steady-state physical grid constraints. Actuating agents representing voltage-controlled distributed energy resources (DERs) in the microgrid exchange information with one another to iteratively compute the optimal local voltage set point. In order to account for physical inter dependencies in the microgrid in a tractable manner, a transformation is applied to the three-phase abc representation of voltage and current to the synchronously rotating dq frame of reference. Resulting linear steady-state voltage and current equations allow for the decomposition of the optimal coordination problem that can be solved by every actuating agent in a highly granular manner. Theoretical and practical studies highlight the effective performance of the proposed algorithm.
{"title":"Optimal Decentralized Coordination of Voltage-Controlled Sources in Islanded Microgrids","authors":"M. Mallick, Pirathayini Srikantha","doi":"10.1109/SmartGridComm.2018.8587507","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2018.8587507","url":null,"abstract":"The recent amalgamation of advanced communication and actuation capabilities into power entities is fundamental for enabling the design of an adaptive, efficient and resilient power grid. In this paper, we focus specifically on optimal and decentralized microgrid coordination that accounts for steady-state physical grid constraints. Actuating agents representing voltage-controlled distributed energy resources (DERs) in the microgrid exchange information with one another to iteratively compute the optimal local voltage set point. In order to account for physical inter dependencies in the microgrid in a tractable manner, a transformation is applied to the three-phase abc representation of voltage and current to the synchronously rotating dq frame of reference. Resulting linear steady-state voltage and current equations allow for the decomposition of the optimal coordination problem that can be solved by every actuating agent in a highly granular manner. Theoretical and practical studies highlight the effective performance of the proposed algorithm.","PeriodicalId":213523,"journal":{"name":"2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123200188","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}
Pub Date : 2018-10-01DOI: 10.1109/SmartGridComm.2018.8587603
J. Parvizi, J. B. Jørgensen, H. Madsen
Integrating flexible consumers in grids with high penetration of renewable energy sources requires a robust power balancing strategy. The methodologies and solutions suggested in this article aim to describe a flexible framework for controlling future electric energy systems by formulating the aggregation problem as a hierarchical robust optimization problem on different aggregation levels. The Aggregator solves a minmax robust optimization problem through a model predictive control framework. With two numercal examples we show how our algorithm controls flexible loads in closed loop, such that consumption follows the stochastic changing production influenced by the penetration of renewables into the power system.
{"title":"Robust Model Predictive Control with Scenarios for Aggregators in Grids with High Penetration of Renewable Energy Sources.","authors":"J. Parvizi, J. B. Jørgensen, H. Madsen","doi":"10.1109/SmartGridComm.2018.8587603","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2018.8587603","url":null,"abstract":"Integrating flexible consumers in grids with high penetration of renewable energy sources requires a robust power balancing strategy. The methodologies and solutions suggested in this article aim to describe a flexible framework for controlling future electric energy systems by formulating the aggregation problem as a hierarchical robust optimization problem on different aggregation levels. The Aggregator solves a minmax robust optimization problem through a model predictive control framework. With two numercal examples we show how our algorithm controls flexible loads in closed loop, such that consumption follows the stochastic changing production influenced by the penetration of renewables into the power system.","PeriodicalId":213523,"journal":{"name":"2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126559068","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}
Pub Date : 2018-10-01DOI: 10.1109/SmartGridComm.2018.8587563
Derek Chang, D. Shelar, Saurabh Amin
Electricity distribution networks (DNs) in many regions are increasingly subjected to disruptions caused by tropical storms. Distributed Energy Resources (DERs) can act as temporary supply sources to sustain “microgrids” resulting from disruptions. In this paper, we investigate the problem of suitable DER allocation to facilitate more efficient repair operations and faster recovery. First, we estimate the failure probabilities of DN components (lines) using a stochastic model of line failures which parametrically depends on the location-specific storm wind field. Next, we formulate a two-stage stochastic mixed integer program, which models the distribution utility’s decision to allocate DERs in the DN (pre-storm stage); and accounts for multi-period decisions on optimal dispatch and line repair scheduling (post-storm stage). A key feature of this formulation is that it jointly optimizes electricity dispatch within the individual microgrids and the line repair schedules to minimize the sum of the cost of DER allocation and cost due to lost load. To illustrate our approach, we use the sample average approximation method to solve our problem for a small-size DN under different storm intensities and DER/crew constraints.
{"title":"DER Allocation and Line Repair Scheduling for Storm-induced Failures in Distribution Networks","authors":"Derek Chang, D. Shelar, Saurabh Amin","doi":"10.1109/SmartGridComm.2018.8587563","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2018.8587563","url":null,"abstract":"Electricity distribution networks (DNs) in many regions are increasingly subjected to disruptions caused by tropical storms. Distributed Energy Resources (DERs) can act as temporary supply sources to sustain “microgrids” resulting from disruptions. In this paper, we investigate the problem of suitable DER allocation to facilitate more efficient repair operations and faster recovery. First, we estimate the failure probabilities of DN components (lines) using a stochastic model of line failures which parametrically depends on the location-specific storm wind field. Next, we formulate a two-stage stochastic mixed integer program, which models the distribution utility’s decision to allocate DERs in the DN (pre-storm stage); and accounts for multi-period decisions on optimal dispatch and line repair scheduling (post-storm stage). A key feature of this formulation is that it jointly optimizes electricity dispatch within the individual microgrids and the line repair schedules to minimize the sum of the cost of DER allocation and cost due to lost load. To illustrate our approach, we use the sample average approximation method to solve our problem for a small-size DN under different storm intensities and DER/crew constraints.","PeriodicalId":213523,"journal":{"name":"2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123883705","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}
Pub Date : 2018-10-01DOI: 10.1109/SmartGridComm.2018.8587472
Adarsh Hasandka, Jianhua Zhang, S. Alam, A. Florita, B. Hodge
The design of reliable, dynamic, fault-tolerant hybrid smart grid communication networks is a challenge to achieve for autonomous power grids. Hybrid networks use different communications technologies for different area networks. A simulation-based parameter optimization framework is proposed to tune parameters of hybrid communication technologies to achieve the optimal network performance. It consists of three main components: a parallel executor used to speedup a list of simulations; a sampler running simulations using the parallel executor at each generation; and a hybrid stochastic optimization algorithm for tuning configurable parameters of hybrid designs and applications. The proposed hybrid metaheuristic optimization algorithm combines an evolutionary algorithm with a gradient method to quickly achieve an approximately global optimum solution. Three optimization test functions are employed to train the adjustable parameters of the hybrid algorithm. Results show the proposed parameter optimization framework can help the designer choose the right hybrid architecture with an optimal parameter set for a large-scale broadband PLC-WiMAX hybrid smart grid communication network.
{"title":"Simulation-based Parameter Optimization Framework for Large-Scale Hybrid Smart Grid Communications Systems Design","authors":"Adarsh Hasandka, Jianhua Zhang, S. Alam, A. Florita, B. Hodge","doi":"10.1109/SmartGridComm.2018.8587472","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2018.8587472","url":null,"abstract":"The design of reliable, dynamic, fault-tolerant hybrid smart grid communication networks is a challenge to achieve for autonomous power grids. Hybrid networks use different communications technologies for different area networks. A simulation-based parameter optimization framework is proposed to tune parameters of hybrid communication technologies to achieve the optimal network performance. It consists of three main components: a parallel executor used to speedup a list of simulations; a sampler running simulations using the parallel executor at each generation; and a hybrid stochastic optimization algorithm for tuning configurable parameters of hybrid designs and applications. The proposed hybrid metaheuristic optimization algorithm combines an evolutionary algorithm with a gradient method to quickly achieve an approximately global optimum solution. Three optimization test functions are employed to train the adjustable parameters of the hybrid algorithm. Results show the proposed parameter optimization framework can help the designer choose the right hybrid architecture with an optimal parameter set for a large-scale broadband PLC-WiMAX hybrid smart grid communication network.","PeriodicalId":213523,"journal":{"name":"2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"482 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116530933","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}
Pub Date : 2018-10-01DOI: 10.1109/SmartGridComm.2018.8587495
José Horta, E. Altman, M. Caujolle, D. Kofman, D. Menga
Future electricity distribution grids will host a considerable share of the renewable energy sources needed for enforcing the energy transition. Demand side management mechanisms play a key role in the integration of such renewable energy resources by exploiting the flexibility of elastic loads, generation or electricity storage technologies. In particular, local energy markets enable households to exchange energy with each other while increasing the amount of renewable energy that is consumed locally. Nevertheless, as most ex-ante mechanisms, local market schedules rely on hour-ahead forecasts whose accuracy may be low. In this paper we cope with forecast errors by proposing a game theory approach to model the interactions among prosumers and distribution system operators for the control of electricity flows in real-time. The presented game has an aggregative equilibrium which can be attained in a semi-distributed manner, driving prosumers towards a final exchange of energy with the grid that benefits both households and operators, favoring the enforcement of prosumers’ local market commitments while respecting the constraints defined by the operator. The proposed mechanism requires only one-to-all broadcast of price signals, which do not depend either on the amount of players or their local objective function and constraints, making the approach highly scalable. Its impact on distribution grid quality of supply was evaluated through load flow analysis and realistic load profiles, demonstrating the capacity of the mechanism ensure that voltage deviation and thermal limit constraints are respected.
{"title":"Real-time enforcement of local energy market transactions respecting distribution grid constraints","authors":"José Horta, E. Altman, M. Caujolle, D. Kofman, D. Menga","doi":"10.1109/SmartGridComm.2018.8587495","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2018.8587495","url":null,"abstract":"Future electricity distribution grids will host a considerable share of the renewable energy sources needed for enforcing the energy transition. Demand side management mechanisms play a key role in the integration of such renewable energy resources by exploiting the flexibility of elastic loads, generation or electricity storage technologies. In particular, local energy markets enable households to exchange energy with each other while increasing the amount of renewable energy that is consumed locally. Nevertheless, as most ex-ante mechanisms, local market schedules rely on hour-ahead forecasts whose accuracy may be low. In this paper we cope with forecast errors by proposing a game theory approach to model the interactions among prosumers and distribution system operators for the control of electricity flows in real-time. The presented game has an aggregative equilibrium which can be attained in a semi-distributed manner, driving prosumers towards a final exchange of energy with the grid that benefits both households and operators, favoring the enforcement of prosumers’ local market commitments while respecting the constraints defined by the operator. The proposed mechanism requires only one-to-all broadcast of price signals, which do not depend either on the amount of players or their local objective function and constraints, making the approach highly scalable. Its impact on distribution grid quality of supply was evaluated through load flow analysis and realistic load profiles, demonstrating the capacity of the mechanism ensure that voltage deviation and thermal limit constraints are respected.","PeriodicalId":213523,"journal":{"name":"2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134568650","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}
Pub Date : 2018-10-01DOI: 10.1109/SmartGridComm.2018.8587480
Niklas Ebell, F. Heinrich, Jonas Schlund, M. Pruckner
Rooftop-installed photovoltaic systems for residential buildings withbattery energy storage system are increasing. Controlling power flows of volatile and unpredictable renewable energy sources in such a system is challenging. Therefore, in this paper we present an algorithm based on Reinforcement Learning to control the power flows of a residential household with a battery energy storage system and a photovoltaic system using neural networks as a function approximation. In a nondeterministic environment the optimal choice of a series of actions to be taken is complex. Training a Reinforcement Learning algorithm, these complex patterns can be learned. The task of the energy storage is to reduce the energy feed-in to the electric grid as well as to improve power system stability by providing frequency containment reserve power to the transmission system operator. Our model includes the profiles of the grid’s frequency, photovoltaic power generation and the electric load of two different households for one year. The first household is used to train the algorithm and to adjust the weights of the neural network to estimate the state-action values. The second household is used to test the functionality of the algorithm on unseen data. To evaluate the behavior of the Reinforcement Learning algorithm the results are compared to a simulation of rule-based control. As a result, after 300 episodes of training, the algorithm is able to reduce the energy consumption from the grid up to 7.8% compared to the rule-based control system managing the system’s power flows.
{"title":"Reinforcement Learning Control Algorithm for a PV-Battery-System Providing Frequency Containment Reserve Power","authors":"Niklas Ebell, F. Heinrich, Jonas Schlund, M. Pruckner","doi":"10.1109/SmartGridComm.2018.8587480","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2018.8587480","url":null,"abstract":"Rooftop-installed photovoltaic systems for residential buildings withbattery energy storage system are increasing. Controlling power flows of volatile and unpredictable renewable energy sources in such a system is challenging. Therefore, in this paper we present an algorithm based on Reinforcement Learning to control the power flows of a residential household with a battery energy storage system and a photovoltaic system using neural networks as a function approximation. In a nondeterministic environment the optimal choice of a series of actions to be taken is complex. Training a Reinforcement Learning algorithm, these complex patterns can be learned. The task of the energy storage is to reduce the energy feed-in to the electric grid as well as to improve power system stability by providing frequency containment reserve power to the transmission system operator. Our model includes the profiles of the grid’s frequency, photovoltaic power generation and the electric load of two different households for one year. The first household is used to train the algorithm and to adjust the weights of the neural network to estimate the state-action values. The second household is used to test the functionality of the algorithm on unseen data. To evaluate the behavior of the Reinforcement Learning algorithm the results are compared to a simulation of rule-based control. As a result, after 300 episodes of training, the algorithm is able to reduce the energy consumption from the grid up to 7.8% compared to the rule-based control system managing the system’s power flows.","PeriodicalId":213523,"journal":{"name":"2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133807090","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}
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