Pub Date : 2016-10-01DOI: 10.1109/PMAPS.2016.7764094
I. B. Sperstad, A. Helseth, M. Korpås
Dynamic optimal power flow (DOPF) models are needed to optimize the operation of a power system with energy storage systems (ESSs) over an extended planning horizon. The optimal storage level at the end of each planning horizon depends on the possible realization of uncertainties in future planning horizons. However, most DOPF models simply require that the storage levels at the end and at the beginning of the planning horizon should be equal. In this paper we consider an AC DOPF model for a distribution system with ESS that explicitly takes into account the expected future value of stored energy. We illustrate the evaluation of the future value function for a system with a wind power plant and demonstrate the use of this value function in the operation of the ESS. The results show that such an operational strategy can be effective compared to not considering the value of stored energy.
{"title":"Valuation of stored energy in dynamic optimal power flow of distribution systems with energy storage","authors":"I. B. Sperstad, A. Helseth, M. Korpås","doi":"10.1109/PMAPS.2016.7764094","DOIUrl":"https://doi.org/10.1109/PMAPS.2016.7764094","url":null,"abstract":"Dynamic optimal power flow (DOPF) models are needed to optimize the operation of a power system with energy storage systems (ESSs) over an extended planning horizon. The optimal storage level at the end of each planning horizon depends on the possible realization of uncertainties in future planning horizons. However, most DOPF models simply require that the storage levels at the end and at the beginning of the planning horizon should be equal. In this paper we consider an AC DOPF model for a distribution system with ESS that explicitly takes into account the expected future value of stored energy. We illustrate the evaluation of the future value function for a system with a wind power plant and demonstrate the use of this value function in the operation of the ESS. The results show that such an operational strategy can be effective compared to not considering the value of stored energy.","PeriodicalId":265474,"journal":{"name":"2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114961633","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 : 2016-10-01DOI: 10.1109/PMAPS.2016.7764155
Wenzu Wu, Kunjin Chen, Ying Qiao, Zongxiang Lu
High-precision wind power forecasting is an essential operation issue of power systems integrated with large numbers of wind farms. In addition to traditional forecasting methods, probabilistic forecasting is recognized as an optimal forecasting solution since it provides a wealth of valuable uncertainty information of wind power. In this paper, a novel approach based on deep neural networks (DNNs) for the deterministic short-term wind power forecasting of wind farms is proposed. DNN models including long short-term memory (LSTM) recurrent neural networks (RNNs) have achieved better results compared with traditional methods. Further, probabilistic forecasting based on conditional error analysis is also implemented. Favorable results of probabilistic forecasting are achieved owing to elaborate division of the conditions set based on cluster analysis. The performance of the proposed method is tested on a dataset of several wind farms in north-east China. Forecasting results are evaluated using different indices, which proves the effectiveness of the proposed method.
{"title":"Probabilistic short-term wind power forecasting based on deep neural networks","authors":"Wenzu Wu, Kunjin Chen, Ying Qiao, Zongxiang Lu","doi":"10.1109/PMAPS.2016.7764155","DOIUrl":"https://doi.org/10.1109/PMAPS.2016.7764155","url":null,"abstract":"High-precision wind power forecasting is an essential operation issue of power systems integrated with large numbers of wind farms. In addition to traditional forecasting methods, probabilistic forecasting is recognized as an optimal forecasting solution since it provides a wealth of valuable uncertainty information of wind power. In this paper, a novel approach based on deep neural networks (DNNs) for the deterministic short-term wind power forecasting of wind farms is proposed. DNN models including long short-term memory (LSTM) recurrent neural networks (RNNs) have achieved better results compared with traditional methods. Further, probabilistic forecasting based on conditional error analysis is also implemented. Favorable results of probabilistic forecasting are achieved owing to elaborate division of the conditions set based on cluster analysis. The performance of the proposed method is tested on a dataset of several wind farms in north-east China. Forecasting results are evaluated using different indices, which proves the effectiveness of the proposed method.","PeriodicalId":265474,"journal":{"name":"2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129885115","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 : 2016-10-01DOI: 10.1109/PMAPS.2016.7764074
J. H. Zheng, X. Quan, Z. Jing, Q. Wu
With more and more uncertain wind power generation integrated in power systems, it is significant to enhance the resilience of generation scheduling to avoid imbalance charges. This paper proposes a stochastic day-ahead generation scheduling (SDAGS) with pumped-storage (PS) stations and wind power (WP) integrated in power systems to tackle the variability of wind power for the purpose of reliability and economy of system operation. Considering the uncertainties of load and wind power generation, Latin hypercube sampling with Cholesky decomposition (LHS-CD) is utilized to generate several scenarios. Multi-objective group search optimizer with adaptive covariance and Lévy flights (MGSO-ACL) is applied to optimize the SDAGS over 24-hour period, aiming at reaching a compromise between the minimization of expectation and variance of total cost of the SDAGS. Furthermore, a decision making method based on evidential reasoning (ER) approach is utilized to determine a final optimal solution considering expected carbon dioxide emission and expected polluted gas emission. Simulation studies are conducted on two different power systems with PS stations and WP integrated to verify the efficiency of the SDAGS.
{"title":"Stochastic day-ahead generation scheduling with pumped-storage stations and wind power integrated","authors":"J. H. Zheng, X. Quan, Z. Jing, Q. Wu","doi":"10.1109/PMAPS.2016.7764074","DOIUrl":"https://doi.org/10.1109/PMAPS.2016.7764074","url":null,"abstract":"With more and more uncertain wind power generation integrated in power systems, it is significant to enhance the resilience of generation scheduling to avoid imbalance charges. This paper proposes a stochastic day-ahead generation scheduling (SDAGS) with pumped-storage (PS) stations and wind power (WP) integrated in power systems to tackle the variability of wind power for the purpose of reliability and economy of system operation. Considering the uncertainties of load and wind power generation, Latin hypercube sampling with Cholesky decomposition (LHS-CD) is utilized to generate several scenarios. Multi-objective group search optimizer with adaptive covariance and Lévy flights (MGSO-ACL) is applied to optimize the SDAGS over 24-hour period, aiming at reaching a compromise between the minimization of expectation and variance of total cost of the SDAGS. Furthermore, a decision making method based on evidential reasoning (ER) approach is utilized to determine a final optimal solution considering expected carbon dioxide emission and expected polluted gas emission. Simulation studies are conducted on two different power systems with PS stations and WP integrated to verify the efficiency of the SDAGS.","PeriodicalId":265474,"journal":{"name":"2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131254775","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 : 2016-10-01DOI: 10.1109/PMAPS.2016.7764200
S. J. Kazempour, P. Pinson
This paper evaluates how different risk preferences of electricity producers alter the market-clearing outcomes. Toward this goal, we propose a stochastic equilibrium model for electricity markets with two settlements, i.e., day-ahead and balancing, in which a number of conventional and stochastic renewable (e.g., wind power) producers compete. We assume that all producers are price-taking and can be risk-averse, while loads are inelastic to price. Renewable power production is the only source of uncertainty considered. The risk of profit variability of each producer is incorporated into the model using the conditional value-at-risk (CVaR) metric. The proposed equilibrium model consists of several risk-constrained profit maximization problems (one per producer), several curtailment cost minimization problems (one per load), and power balance constraints. Each optimization problem is then replaced by its optimality conditions, resulting in a mixed complementarity problem. Numerical results from a case study based on the IEEE one-area reliability test system are derived and discussed.
{"title":"Effects of risk aversion on market outcomes: A stochastic two-stage equilibrium model","authors":"S. J. Kazempour, P. Pinson","doi":"10.1109/PMAPS.2016.7764200","DOIUrl":"https://doi.org/10.1109/PMAPS.2016.7764200","url":null,"abstract":"This paper evaluates how different risk preferences of electricity producers alter the market-clearing outcomes. Toward this goal, we propose a stochastic equilibrium model for electricity markets with two settlements, i.e., day-ahead and balancing, in which a number of conventional and stochastic renewable (e.g., wind power) producers compete. We assume that all producers are price-taking and can be risk-averse, while loads are inelastic to price. Renewable power production is the only source of uncertainty considered. The risk of profit variability of each producer is incorporated into the model using the conditional value-at-risk (CVaR) metric. The proposed equilibrium model consists of several risk-constrained profit maximization problems (one per producer), several curtailment cost minimization problems (one per load), and power balance constraints. Each optimization problem is then replaced by its optimality conditions, resulting in a mixed complementarity problem. Numerical results from a case study based on the IEEE one-area reliability test system are derived and discussed.","PeriodicalId":265474,"journal":{"name":"2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129847340","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 : 2016-10-01DOI: 10.1109/PMAPS.2016.7764060
Mao Yang, Chunlin Yang
Wind energy is supplying an increasing proportion of demand in the electrical grid. An accompanied problem is that the operational reliability of the power system is affected by the inherent uncertainty and stochastic variation of wind generation which also leads to the wind power forecasts of low accuracy. Therefore, the point prediction of wind power produced by a traditional deterministic forecasting model having a low level of confidence could not reflect the uncertainty of wind generation which could not meet the requirements for the safe operation of a power system. This paper aims to use the method of the non-parametric estimation to model the probability density distribution of the errors of wind power forecasts and determine the regression function based on the estimated point or deterministic wind power forecasts. The intervals of wind power predictions reaching a certain level of confidence can be employed by system operators to estimate the operation costs and the potential risks.
{"title":"Uncertainty analysis of wind power prediction based on Granular Computing","authors":"Mao Yang, Chunlin Yang","doi":"10.1109/PMAPS.2016.7764060","DOIUrl":"https://doi.org/10.1109/PMAPS.2016.7764060","url":null,"abstract":"Wind energy is supplying an increasing proportion of demand in the electrical grid. An accompanied problem is that the operational reliability of the power system is affected by the inherent uncertainty and stochastic variation of wind generation which also leads to the wind power forecasts of low accuracy. Therefore, the point prediction of wind power produced by a traditional deterministic forecasting model having a low level of confidence could not reflect the uncertainty of wind generation which could not meet the requirements for the safe operation of a power system. This paper aims to use the method of the non-parametric estimation to model the probability density distribution of the errors of wind power forecasts and determine the regression function based on the estimated point or deterministic wind power forecasts. The intervals of wind power predictions reaching a certain level of confidence can be employed by system operators to estimate the operation costs and the potential risks.","PeriodicalId":265474,"journal":{"name":"2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132369151","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 : 2016-10-01DOI: 10.1109/PMAPS.2016.7764146
D. Clements, P. Mancarella, R. Ash
Underground cables have thermal inertia that can be leveraged to tolerate loading beyond 100% of capacity for short periods of time. These short term overloads allow the calculation of time-limited ratings for cables that are routinely underutilized such as those in N-1 configurations. These ratings are often not considered as part of distribution network modelling and only sometimes applied by network operators. Recent advances in cable rating technology allow network operators to calculate time-limited ratings in real time to adapt to contingency situations on their network. This paper proposes a methodology for determining the benefits of using time-limited ratings on an 11kV ring network. A case study shows how increasing loadings can be mitigated by the use of time-limited ratings and how this affects the economics of operating and planning a power system, including for avoiding network reinforcement.
{"title":"Application of time-limited ratings to underground cables to enable life extension of network assets","authors":"D. Clements, P. Mancarella, R. Ash","doi":"10.1109/PMAPS.2016.7764146","DOIUrl":"https://doi.org/10.1109/PMAPS.2016.7764146","url":null,"abstract":"Underground cables have thermal inertia that can be leveraged to tolerate loading beyond 100% of capacity for short periods of time. These short term overloads allow the calculation of time-limited ratings for cables that are routinely underutilized such as those in N-1 configurations. These ratings are often not considered as part of distribution network modelling and only sometimes applied by network operators. Recent advances in cable rating technology allow network operators to calculate time-limited ratings in real time to adapt to contingency situations on their network. This paper proposes a methodology for determining the benefits of using time-limited ratings on an 11kV ring network. A case study shows how increasing loadings can be mitigated by the use of time-limited ratings and how this affects the economics of operating and planning a power system, including for avoiding network reinforcement.","PeriodicalId":265474,"journal":{"name":"2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130123385","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 : 2016-10-01DOI: 10.1109/PMAPS.2016.7764206
M. N. Hjelmeland, C. T. Larsen, M. Korpås, A. Helseth
This paper investigates how wind power can contribute to the provision of rotating reserves in a hydro-dominated power system with limited transmission capacity to an exogenous power market. We emphasize on the impacts different schemes for providing rotating reserves has on the generation dispatch and rotating reserve (RR) cost. Due to the flexibility provided by hydropower, the system is well suited for facilitating a large share of intermittent energy. We approached this by building a model based on Stochastic Dual Dynamic Programming (SDDP), which efficiently handles multistage stochastic problems. A case study is presented based on the properties from the Nordic power system. Results shows that for wind penetration levels above 20%, some wind power is used for the provision of upwards RR at higher costs than the hydropower could provide, but freeing up more flexibility for the hydropower units and subsequently higher overall gain. The use of wind power to provide downwards RR proved to be very cost efficient, as there is no opportunity cost associated with the use of wind power.
{"title":"Provision of rotating reserves from wind power in a hydro-dominated power system","authors":"M. N. Hjelmeland, C. T. Larsen, M. Korpås, A. Helseth","doi":"10.1109/PMAPS.2016.7764206","DOIUrl":"https://doi.org/10.1109/PMAPS.2016.7764206","url":null,"abstract":"This paper investigates how wind power can contribute to the provision of rotating reserves in a hydro-dominated power system with limited transmission capacity to an exogenous power market. We emphasize on the impacts different schemes for providing rotating reserves has on the generation dispatch and rotating reserve (RR) cost. Due to the flexibility provided by hydropower, the system is well suited for facilitating a large share of intermittent energy. We approached this by building a model based on Stochastic Dual Dynamic Programming (SDDP), which efficiently handles multistage stochastic problems. A case study is presented based on the properties from the Nordic power system. Results shows that for wind penetration levels above 20%, some wind power is used for the provision of upwards RR at higher costs than the hydropower could provide, but freeing up more flexibility for the hydropower units and subsequently higher overall gain. The use of wind power to provide downwards RR proved to be very cost efficient, as there is no opportunity cost associated with the use of wind power.","PeriodicalId":265474,"journal":{"name":"2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130082610","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 : 2016-10-01DOI: 10.1109/PMAPS.2016.7764145
M. Matos, R. Bessa, C. Gonçalves, L. Cavalcante, Vladimiro Miranda, N. Machado, P. Marques, F. Matos
In order to reduce the curtailment of renewable generation in periods of low load, operators can limit the import net transfer capacity (NTC) of interconnections. This paper presents a probabilistic approach to support the operator in setting the maximum import NTC value in a way that the risk of curtailment remains below a pre-specified threshold. Main inputs are the probabilistic forecasts of wind power and solar PV generation, and special care is taken regarding the tails of the global margin distribution (all generation - all loads and pumping), since the accepted thresholds are generally very low. Two techniques are used for this purpose: interpolation with exponential functions and nonparametric estimation of extreme conditional quantiles using extreme value theory. The methodology is applied to five representative days, where situations ranging from high maximum NTC values to NTC=0 are addressed. Comparison of the two techniques for modeling tails is also comprised.
{"title":"Setting the maximum import net transfer capacity under extreme RES integration scenarios","authors":"M. Matos, R. Bessa, C. Gonçalves, L. Cavalcante, Vladimiro Miranda, N. Machado, P. Marques, F. Matos","doi":"10.1109/PMAPS.2016.7764145","DOIUrl":"https://doi.org/10.1109/PMAPS.2016.7764145","url":null,"abstract":"In order to reduce the curtailment of renewable generation in periods of low load, operators can limit the import net transfer capacity (NTC) of interconnections. This paper presents a probabilistic approach to support the operator in setting the maximum import NTC value in a way that the risk of curtailment remains below a pre-specified threshold. Main inputs are the probabilistic forecasts of wind power and solar PV generation, and special care is taken regarding the tails of the global margin distribution (all generation - all loads and pumping), since the accepted thresholds are generally very low. Two techniques are used for this purpose: interpolation with exponential functions and nonparametric estimation of extreme conditional quantiles using extreme value theory. The methodology is applied to five representative days, where situations ranging from high maximum NTC values to NTC=0 are addressed. Comparison of the two techniques for modeling tails is also comprised.","PeriodicalId":265474,"journal":{"name":"2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130201862","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 : 2016-10-01DOI: 10.1109/PMAPS.2016.7764190
G. Doğan, P. Labeau, J. Maun, J. Sprooten, M. Galvez, K. Sleurs
With the increasing amount of renewable and difficult-to-forecast generation units, Transmission System Operators (TSO) are facing new challenges to operate the grid properly. Indeed, given the intrinsic variability and limited predictability of most renewable generations, the application of the conventional and deterministic N-1 method becomes very costly. Therefore, a new approach is needed for system operational planning. This paper presents a method that combines the advantages of probabilistic and deterministic approaches in order to estimate risk indicators while considering errors on weather (hence generation) forecasts, uncertainties on loads and timing constraints of the decision-making process in operational planning. This decision support method provides the planner with indicators to analyze, improve and finally, validate a grid plan. The method has been tested and its results have been compared with the classical N-1 analysis. Results show that the method offers more indicators to help the planner and to compare different grid plans.
{"title":"Discrete forecast error scenarios methodology for grid reliabitity assessment in short-term planning","authors":"G. Doğan, P. Labeau, J. Maun, J. Sprooten, M. Galvez, K. Sleurs","doi":"10.1109/PMAPS.2016.7764190","DOIUrl":"https://doi.org/10.1109/PMAPS.2016.7764190","url":null,"abstract":"With the increasing amount of renewable and difficult-to-forecast generation units, Transmission System Operators (TSO) are facing new challenges to operate the grid properly. Indeed, given the intrinsic variability and limited predictability of most renewable generations, the application of the conventional and deterministic N-1 method becomes very costly. Therefore, a new approach is needed for system operational planning. This paper presents a method that combines the advantages of probabilistic and deterministic approaches in order to estimate risk indicators while considering errors on weather (hence generation) forecasts, uncertainties on loads and timing constraints of the decision-making process in operational planning. This decision support method provides the planner with indicators to analyze, improve and finally, validate a grid plan. The method has been tested and its results have been compared with the classical N-1 analysis. Results show that the method offers more indicators to help the planner and to compare different grid plans.","PeriodicalId":265474,"journal":{"name":"2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130244733","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 : 2016-10-01DOI: 10.1109/PMAPS.2016.7764076
Zhongwen Li, C. Zang, P. Zeng, Haibin Yu, Hepeng Li
Microgrids (MGs) are presented as a cornerstone of smart grid, which can integrate intermittent renewable energy sources (RES), storage system, and local loads environmentally and reliably. Due to the randomness in RES and load, a great challenge lies in the optimal operation of MGs. Two-stage stochastic programming (SP) can involve the forecast uncertainties of load demand, photovoltaic (PV) and wind production in the optimization model. Thus, through two-stage SP, a more robust scheduling plan is derived, which minimizes the risk from the impact of uncertainties. The model predictive control (MPC) can effectively avoid short sighting and further compensate the uncertainty within the MG through a feedback mechanism. In this paper, a two-stage SP based MPC stratey is proposed for microgrid energy management under uncertainties, which combines the advantages of both two-stage SP and MPC. The results of numerical experiments explicitly demonstrate the benefits of the proposed strategy.
{"title":"Two-stage stochastic programming based model predictive control strategy for microgrid energy management under uncertainties","authors":"Zhongwen Li, C. Zang, P. Zeng, Haibin Yu, Hepeng Li","doi":"10.1109/PMAPS.2016.7764076","DOIUrl":"https://doi.org/10.1109/PMAPS.2016.7764076","url":null,"abstract":"Microgrids (MGs) are presented as a cornerstone of smart grid, which can integrate intermittent renewable energy sources (RES), storage system, and local loads environmentally and reliably. Due to the randomness in RES and load, a great challenge lies in the optimal operation of MGs. Two-stage stochastic programming (SP) can involve the forecast uncertainties of load demand, photovoltaic (PV) and wind production in the optimization model. Thus, through two-stage SP, a more robust scheduling plan is derived, which minimizes the risk from the impact of uncertainties. The model predictive control (MPC) can effectively avoid short sighting and further compensate the uncertainty within the MG through a feedback mechanism. In this paper, a two-stage SP based MPC stratey is proposed for microgrid energy management under uncertainties, which combines the advantages of both two-stage SP and MPC. The results of numerical experiments explicitly demonstrate the benefits of the proposed strategy.","PeriodicalId":265474,"journal":{"name":"2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131781225","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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