Pub Date : 2018-10-01DOI: 10.1109/SmartGridComm.2018.8587550
Zachary J. Lee, Daniel Chang, Cheng Jin, George S. Lee, Rand Lee, Ted Lee, S. Low
Large-scale charging infrastructure will play an important role in supporting the adoption of electric vehicles. In this paper, we address the prohibitively high capital cost of installing large numbers of charging stations within a parking facility by oversubscribing key pieces of electrical infrastructure. We describe a unique physical testbed for large-scale, high- density EV charging research which we call the Adaptive Charging Network (ACN). We describe the architecture of the ACN including its hardware and software components. We also present a practical framework for online scheduling, which is based on model predictive control and convex optimization. Based on our experience with practical EV charging systems, we introduce constraints to the EV charging problem which have not been considered in the literature, such as those imposed by unbalanced three-phase infrastructure. We use simulations based on real data collected from the ACN to illustrate the trade-offs involved in selecting models for infrastructure constraints and accounting for non-ideal charging behavior.
{"title":"Large-Scale Adaptive Electric Vehicle Charging","authors":"Zachary J. Lee, Daniel Chang, Cheng Jin, George S. Lee, Rand Lee, Ted Lee, S. Low","doi":"10.1109/SmartGridComm.2018.8587550","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2018.8587550","url":null,"abstract":"Large-scale charging infrastructure will play an important role in supporting the adoption of electric vehicles. In this paper, we address the prohibitively high capital cost of installing large numbers of charging stations within a parking facility by oversubscribing key pieces of electrical infrastructure. We describe a unique physical testbed for large-scale, high- density EV charging research which we call the Adaptive Charging Network (ACN). We describe the architecture of the ACN including its hardware and software components. We also present a practical framework for online scheduling, which is based on model predictive control and convex optimization. Based on our experience with practical EV charging systems, we introduce constraints to the EV charging problem which have not been considered in the literature, such as those imposed by unbalanced three-phase infrastructure. We use simulations based on real data collected from the ACN to illustrate the trade-offs involved in selecting models for infrastructure constraints and accounting for non-ideal charging behavior.","PeriodicalId":213523,"journal":{"name":"2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"24 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":"129088592","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.2018.8587483
Mohamed Amine Abid, H. Meer
Faced with today’s energetic challenges, the management of Smart Cities is becoming more and more challenging. Local management of individual buildings is proven to be complex and inefficient. A new vision that promotes more flexibility and collaboration between buildings has become a must. In this paper, a new concept, called ”Virtualized Software-Defined Buildings (VSDB)” is introduced. It is presented as a key enabler of future Smart Cities. In fact, through virtualization, it helps in setting up multiple systems independently from the underlying physical infrastructure, offering the needed flexibility at reduced costs. A showcase example is presented to illustrate the potential of this new concept compared to the traditional management solutions.
{"title":"Virtualized Software Defined Buildings: a Key Enabler of The Future Smart Cities","authors":"Mohamed Amine Abid, H. Meer","doi":"10.1109/SmartGridComm.2018.8587483","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2018.8587483","url":null,"abstract":"Faced with today’s energetic challenges, the management of Smart Cities is becoming more and more challenging. Local management of individual buildings is proven to be complex and inefficient. A new vision that promotes more flexibility and collaboration between buildings has become a must. In this paper, a new concept, called ”Virtualized Software-Defined Buildings (VSDB)” is introduced. It is presented as a key enabler of future Smart Cities. In fact, through virtualization, it helps in setting up multiple systems independently from the underlying physical infrastructure, offering the needed flexibility at reduced costs. A showcase example is presented to illustrate the potential of this new concept compared to the traditional management solutions.","PeriodicalId":213523,"journal":{"name":"2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"21 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":"121078282","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.2018.8587520
{"title":"[Copyright notice]","authors":"","doi":"10.1109/smartgridcomm.2018.8587520","DOIUrl":"https://doi.org/10.1109/smartgridcomm.2018.8587520","url":null,"abstract":"","PeriodicalId":213523,"journal":{"name":"2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"18 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":"124108872","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.2018.8587553
Zhenwei Guo, Qinmin Yang, Zaiyue Yang
The scheduling of appliance power consumption is one of the main tasks in demand response management in smart grids. In many scenarios, it requires us to optimally schedule a large number of appliances with limited computational resources, thus the computational efficiency becomes a major concern of algorithm design. To this end, a novel algorithm is proposed based on KKT conditions to solve the optimal power scheduling problem with temporally-spatially coupled constraints. We show the algorithm is much more efficient than conventional algorithms, e.g., dual decomposition, and less sensitive to the problem parameter setting, as verified by numerical examples.
{"title":"A Fast Algorithm for Optimal Power Scheduling of Large-Scale Appliances with Temporally-Spatially Coupled Constraints","authors":"Zhenwei Guo, Qinmin Yang, Zaiyue Yang","doi":"10.1109/SmartGridComm.2018.8587553","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2018.8587553","url":null,"abstract":"The scheduling of appliance power consumption is one of the main tasks in demand response management in smart grids. In many scenarios, it requires us to optimally schedule a large number of appliances with limited computational resources, thus the computational efficiency becomes a major concern of algorithm design. To this end, a novel algorithm is proposed based on KKT conditions to solve the optimal power scheduling problem with temporally-spatially coupled constraints. We show the algorithm is much more efficient than conventional algorithms, e.g., dual decomposition, and less sensitive to the problem parameter setting, as verified by numerical examples.","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":"129634281","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.2018.8587421
O. Saukh, F. Papst, S. Saukh
The rise of distributed energy generation technologies along with grid constraints, and conventional non-consumer centric business models, is leading many to explore alternative configurations of the energy system. Particularly popular are peer-to-peer energy trading models in which the role of the energy company is replaced with a trustless transaction layer based on a public blockchain. However, to ensure stable operation of microgrids, an energy company is required to constantly balance supply and demand. In this paper, we study the problem that arises from the conflicting goals of prosumers (to make money) and network operators (to keep the network stable) that have to co-exist in future energy systems. We show that prosumers can play large-scale synchronization games to benefit from the system. If they synchronize their actions to artificially increase energy demand on the market, the resulting power peaks will force the microgrid operator to use backup generation capacities and, as a consequence, contribute to the increased profit margins for prosumers. We study synchronization games from a game-theoretical point of view and argue that even non-cooperative selfish prosumers can learn to play synchronization games independently and enforce undesired outcomes for consumers and the grid. We build a simple model where prosumers independently run Q-learning algorithms to learn their most profitable strategies and show that synchronization games constitute a Nash equilibrium. We discuss implications of our findings and argue the necessity of appropriate mechanism design for stable microgrid operation.
{"title":"Synchronization Games in P2P Energy Trading","authors":"O. Saukh, F. Papst, S. Saukh","doi":"10.1109/SmartGridComm.2018.8587421","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2018.8587421","url":null,"abstract":"The rise of distributed energy generation technologies along with grid constraints, and conventional non-consumer centric business models, is leading many to explore alternative configurations of the energy system. Particularly popular are peer-to-peer energy trading models in which the role of the energy company is replaced with a trustless transaction layer based on a public blockchain. However, to ensure stable operation of microgrids, an energy company is required to constantly balance supply and demand. In this paper, we study the problem that arises from the conflicting goals of prosumers (to make money) and network operators (to keep the network stable) that have to co-exist in future energy systems. We show that prosumers can play large-scale synchronization games to benefit from the system. If they synchronize their actions to artificially increase energy demand on the market, the resulting power peaks will force the microgrid operator to use backup generation capacities and, as a consequence, contribute to the increased profit margins for prosumers. We study synchronization games from a game-theoretical point of view and argue that even non-cooperative selfish prosumers can learn to play synchronization games independently and enforce undesired outcomes for consumers and the grid. We build a simple model where prosumers independently run Q-learning algorithms to learn their most profitable strategies and show that synchronization games constitute a Nash equilibrium. We discuss implications of our findings and argue the necessity of appropriate mechanism design for stable microgrid operation.","PeriodicalId":213523,"journal":{"name":"2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"8 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":"129820951","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.2018.8587593
Shiyao Zhang, Ka-Cheong Leung
The collection of electric vehicles (EVs) can be regarded as a massive storage to the power grid so as to provide vehicle-to-grid (V2G) ancillary services, such as frequency regulation. In this paper, a novel hierarchical framework for joint optimal power flow routing and decentralized scheduling with V2G regulation services is proposed. First, the optimal power flow is formulated by incorporating with power flow routers (PFRs). The problem is solved through the semidefinite programming (SDP) relaxation to pursue the optimal solution. Second, the scheduling problem with V2G regulation service is proposed as a convex optimization problem. The related decentralized algorithm is then devised in order to find the schedules of EVs. Our simulation results show that voltage regulation is effectively achieved and PFRs can help reduce the apparent power loss of the system significantly. In addition, the decentralized scheduling algorithm with V2G regulation service can smooth out the power fluctuations at the buses attached with EVs.
{"title":"Joint Optimal Power Flow Routing and Decentralized Scheduling with Vehicle-to-Grid Regulation Service","authors":"Shiyao Zhang, Ka-Cheong Leung","doi":"10.1109/SmartGridComm.2018.8587593","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2018.8587593","url":null,"abstract":"The collection of electric vehicles (EVs) can be regarded as a massive storage to the power grid so as to provide vehicle-to-grid (V2G) ancillary services, such as frequency regulation. In this paper, a novel hierarchical framework for joint optimal power flow routing and decentralized scheduling with V2G regulation services is proposed. First, the optimal power flow is formulated by incorporating with power flow routers (PFRs). The problem is solved through the semidefinite programming (SDP) relaxation to pursue the optimal solution. Second, the scheduling problem with V2G regulation service is proposed as a convex optimization problem. The related decentralized algorithm is then devised in order to find the schedules of EVs. Our simulation results show that voltage regulation is effectively achieved and PFRs can help reduce the apparent power loss of the system significantly. In addition, the decentralized scheduling algorithm with V2G regulation service can smooth out the power fluctuations at the buses attached with EVs.","PeriodicalId":213523,"journal":{"name":"2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"38 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":"114952485","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.2018.8587444
J. Petersen, J. Bendtsen, Pierre J. C. Vogler-Finck, J. Stoustrup
In this paper we propose a scheme for managing energy flexibility in buildings with significant thermal masses and centralized climate control, such as commercial buildings, which can be used to provide ancillary services to the local electrical system (demand response). The scheme relies on being able to manipulate the forward flow temperature in the climate control system along with heating/cooling of zones of the building, and thereby controlling the electrical power consumption of the system. A Model Predictive Control law is formulated to provide pre-storage of thermal energy in the manipulated zones without violating comfort requirements. The scheme is illustrated on a case study of a Danish shopping center, from which actual heating/cooling data have been collected for identification of thermal dynamics. The Coefficient of Performance of the system’s chiller is assumed to have a known dependence on flow and temperature, which is exploited to relate electrical power consumption to forward flow temperature. Simulation studies indicate potentials for significant power curtailment, in the order of 100 kW for one hour for the shopping center as a whole.
{"title":"Energy Flexibility for Systems with large Thermal Masses with Applications to Shopping Centers","authors":"J. Petersen, J. Bendtsen, Pierre J. C. Vogler-Finck, J. Stoustrup","doi":"10.1109/SmartGridComm.2018.8587444","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2018.8587444","url":null,"abstract":"In this paper we propose a scheme for managing energy flexibility in buildings with significant thermal masses and centralized climate control, such as commercial buildings, which can be used to provide ancillary services to the local electrical system (demand response). The scheme relies on being able to manipulate the forward flow temperature in the climate control system along with heating/cooling of zones of the building, and thereby controlling the electrical power consumption of the system. A Model Predictive Control law is formulated to provide pre-storage of thermal energy in the manipulated zones without violating comfort requirements. The scheme is illustrated on a case study of a Danish shopping center, from which actual heating/cooling data have been collected for identification of thermal dynamics. The Coefficient of Performance of the system’s chiller is assumed to have a known dependence on flow and temperature, which is exploited to relate electrical power consumption to forward flow temperature. Simulation studies indicate potentials for significant power curtailment, in the order of 100 kW for one hour for the shopping center as a whole.","PeriodicalId":213523,"journal":{"name":"2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"82 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":"115030215","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.2018.8587499
M. T. Ahmed, P. Faria, Z. Vale
The peak demand reduction during peak hour is a challenge to the retail energy providers. Demand response program plays a major role to fulfil this purpose. The small household appliances like electric water heater can participate in the demand response program by aggregating it in the smart building energy management system. This paper discusses demand response possibilities of a residential electric water heater, the overall consumption profile, temperature profile and the financial benefit in the consumer level. The direct load control demand response method in yearly timeframe is proposed and applied. Realtime electricity pricing with incentive-based demand response is considered and applied to the direct load control with financial benefit to the consumers. The study includes the difference between normal consumption and consumption after using DLC, normal temperature profile and temperature profiling after DLC. The results exhibit that there is significant energy consumption reduction in the consumer level without making any discomfort.
{"title":"Financial Benefit Analysis of an Electric Water Heater with Direct Load Control in Demand Response","authors":"M. T. Ahmed, P. Faria, Z. Vale","doi":"10.1109/SmartGridComm.2018.8587499","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2018.8587499","url":null,"abstract":"The peak demand reduction during peak hour is a challenge to the retail energy providers. Demand response program plays a major role to fulfil this purpose. The small household appliances like electric water heater can participate in the demand response program by aggregating it in the smart building energy management system. This paper discusses demand response possibilities of a residential electric water heater, the overall consumption profile, temperature profile and the financial benefit in the consumer level. The direct load control demand response method in yearly timeframe is proposed and applied. Realtime electricity pricing with incentive-based demand response is considered and applied to the direct load control with financial benefit to the consumers. The study includes the difference between normal consumption and consumption after using DLC, normal temperature profile and temperature profiling after DLC. The results exhibit that there is significant energy consumption reduction in the consumer level without making any discomfort.","PeriodicalId":213523,"journal":{"name":"2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"13 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":"125244980","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.2018.8587572
Angelo Pinto, T. Pinto, Isabel Praça, Z. Vale, P. Faria
Electricity markets are complex and dynamic environments, mostly due to the large scale integration of renewable energy sources in the system. Negotiation in these markets is a significant challenge, especially when considering negotiations at the local level (e.g., between buildings and distributed energy resources). It is essential for a negotiator to be able to identify the negotiation profile of the players with whom he is negotiating. If a negotiator knows these profiles, it is possible to adapt the negotiation strategy and get better results in a negotiation. In order to identify and define such negotiation profiles, a clustering process is proposed in this paper. The clustering process is performed using the kml-k-means algorithm, in which several negotiation approaches are evaluated in order to identify and define players’ negotiation profiles. A case study is presented, using as input data, information from proposals made during a set of negotiations. Results show that the proposed approach is able to identify players’ negotiation profiles used in bilateral negotiations in electricity markets.
{"title":"Clustering-based negotiation profiles definition for local energy transactions","authors":"Angelo Pinto, T. Pinto, Isabel Praça, Z. Vale, P. Faria","doi":"10.1109/SmartGridComm.2018.8587572","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2018.8587572","url":null,"abstract":"Electricity markets are complex and dynamic environments, mostly due to the large scale integration of renewable energy sources in the system. Negotiation in these markets is a significant challenge, especially when considering negotiations at the local level (e.g., between buildings and distributed energy resources). It is essential for a negotiator to be able to identify the negotiation profile of the players with whom he is negotiating. If a negotiator knows these profiles, it is possible to adapt the negotiation strategy and get better results in a negotiation. In order to identify and define such negotiation profiles, a clustering process is proposed in this paper. The clustering process is performed using the kml-k-means algorithm, in which several negotiation approaches are evaluated in order to identify and define players’ negotiation profiles. A case study is presented, using as input data, information from proposals made during a set of negotiations. Results show that the proposed approach is able to identify players’ negotiation profiles used in bilateral negotiations in electricity markets.","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":"122692342","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.2018.8587558
A. Bagchi, Yunjian Xu
We propose a new approach for the optimal dayahead (DA) market bidding strategy of an aggregator of a power distribution system (with wind and solar generation). The proposed approach incorporates the stochasticity of renewable generation through a distributionally-robust chance-constraint (DRCC), which guarantees that the real-time (RT) energy shortfall (resulting from the DA market commitment and unexpected realization of renewable generation) does not exceed a pre-determined threshold with high probability, even without accurate information about the probability distribution of the random renewable generation. The formulated cost-minimization problem with DRCC is transformed into a deterministic, convex optimization problem. Numerical results demonstrate that the proposed approach enables the aggregator to efficiently trade-off profitability and risk, and that a properly chosen risk tolerance level (in the DRCC) can significantly reduce the average cost at DA market by 6–18.6% (compared with the robust solution), at the cost of negligible probability that the RT energy shortfall exceeds the pre-determined threshold.
{"title":"Distributionally Robust Chance-Constrained Bidding Strategy for Distribution System Aggregator in Day-Ahead Markets","authors":"A. Bagchi, Yunjian Xu","doi":"10.1109/SmartGridComm.2018.8587558","DOIUrl":"https://doi.org/10.1109/SmartGridComm.2018.8587558","url":null,"abstract":"We propose a new approach for the optimal dayahead (DA) market bidding strategy of an aggregator of a power distribution system (with wind and solar generation). The proposed approach incorporates the stochasticity of renewable generation through a distributionally-robust chance-constraint (DRCC), which guarantees that the real-time (RT) energy shortfall (resulting from the DA market commitment and unexpected realization of renewable generation) does not exceed a pre-determined threshold with high probability, even without accurate information about the probability distribution of the random renewable generation. The formulated cost-minimization problem with DRCC is transformed into a deterministic, convex optimization problem. Numerical results demonstrate that the proposed approach enables the aggregator to efficiently trade-off profitability and risk, and that a properly chosen risk tolerance level (in the DRCC) can significantly reduce the average cost at DA market by 6–18.6% (compared with the robust solution), at the cost of negligible probability that the RT energy shortfall exceeds the pre-determined threshold.","PeriodicalId":213523,"journal":{"name":"2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","volume":"2020 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":"116293811","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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