RSM-Based Approximate Dynamic Programming for Stochastic Energy Management of Power Systems
The stochastic energy management (SEM) of power systems is computationally intractable due to its randomness, nonconvexity, and nonlinearity. To solve this problem, a response surface method (RSM)-based approximate dynamic programming (ADP) algorithm is proposed in this paper. Since the value functi...
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| Veröffentlicht in: | IEEE transactions on power systems 2023-11, Vol.38 (6), p.1-13 |
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| creator | Zhuo, Yelin Zhu, Jianquan Chen, Jiajun Wang, Zeshuang Ye, Hanfang Liu, Haixin Liu, Mingbo |
| description | The stochastic energy management (SEM) of power systems is computationally intractable due to its randomness, nonconvexity, and nonlinearity. To solve this problem, a response surface method (RSM)-based approximate dynamic programming (ADP) algorithm is proposed in this paper. Since the value function can be directly obtained by RSM, the proposed algorithm does not need to iteratively approach them as existing ADP algorithms, which facilitates reducing the computing time. In addition, an improved generalized polynomial chaos (IgPC) method (i.e., an extension of RSM) is proposed to calculate the expectation of the value function of ADP in the stochastic environment. Compared with the Monte Carlo method, which is commonly used in existing ADP algorithms, IgPC requires fewer sampling scenarios while providing similar results. Simulation results with two modified IEEE test systems and a real 2778-bus system demonstrate the effectiveness of the proposed algorithm in terms of accuracy and computation efficiency. |
| doi_str_mv | 10.1109/TPWRS.2022.3227345 |
| format | Article |
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To solve this problem, a response surface method (RSM)-based approximate dynamic programming (ADP) algorithm is proposed in this paper. Since the value function can be directly obtained by RSM, the proposed algorithm does not need to iteratively approach them as existing ADP algorithms, which facilitates reducing the computing time. In addition, an improved generalized polynomial chaos (IgPC) method (i.e., an extension of RSM) is proposed to calculate the expectation of the value function of ADP in the stochastic environment. Compared with the Monte Carlo method, which is commonly used in existing ADP algorithms, IgPC requires fewer sampling scenarios while providing similar results. Simulation results with two modified IEEE test systems and a real 2778-bus system demonstrate the effectiveness of the proposed algorithm in terms of accuracy and computation efficiency.</description><identifier>ISSN: 0885-8950</identifier><identifier>EISSN: 1558-0679</identifier><identifier>DOI: 10.1109/TPWRS.2022.3227345</identifier><identifier>CODEN: ITPSEG</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Algorithms ; Chebyshev approximation ; Computing time ; Dynamic programming ; Energy management ; Heuristic algorithms ; improved approximate dynamic programming ; improved generalized polynomial chaos ; Monte Carlo simulation ; Polynomials ; Power system dynamics ; Power systems ; Randomness ; response surface method ; Response surface methodology ; Stochastic energy management ; System effectiveness ; Uncertainty</subject><ispartof>IEEE transactions on power systems, 2023-11, Vol.38 (6), p.1-13</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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To solve this problem, a response surface method (RSM)-based approximate dynamic programming (ADP) algorithm is proposed in this paper. Since the value function can be directly obtained by RSM, the proposed algorithm does not need to iteratively approach them as existing ADP algorithms, which facilitates reducing the computing time. In addition, an improved generalized polynomial chaos (IgPC) method (i.e., an extension of RSM) is proposed to calculate the expectation of the value function of ADP in the stochastic environment. Compared with the Monte Carlo method, which is commonly used in existing ADP algorithms, IgPC requires fewer sampling scenarios while providing similar results. Simulation results with two modified IEEE test systems and a real 2778-bus system demonstrate the effectiveness of the proposed algorithm in terms of accuracy and computation efficiency.</description><subject>Algorithms</subject><subject>Chebyshev approximation</subject><subject>Computing time</subject><subject>Dynamic programming</subject><subject>Energy management</subject><subject>Heuristic algorithms</subject><subject>improved approximate dynamic programming</subject><subject>improved generalized polynomial chaos</subject><subject>Monte Carlo simulation</subject><subject>Polynomials</subject><subject>Power system dynamics</subject><subject>Power systems</subject><subject>Randomness</subject><subject>response surface method</subject><subject>Response surface methodology</subject><subject>Stochastic energy management</subject><subject>System effectiveness</subject><subject>Uncertainty</subject><issn>0885-8950</issn><issn>1558-0679</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kEtPAjEUhRujiYj-Ad00cT3Yx3TaLhFBTSASwLhw0ZROZxziTLEdovPvLUJc3eSec-7jA-AaowHGSN6t5m-L5YAgQgaUEE5TdgJ6mDGRoIzLU9BDQrBESIbOwUUIG4RQFoUeeF8sZ8m9DjaHw-3Wu5-q1q2FD12j68rAuXel13VdNSUsnIfL1pkPHdoojRvryw7OdKNLW9umha6Ac_dto6sLra3DJTgr9GewV8faB6-T8Wr0lExfHp9Hw2liiGRtYuwaC5HlHGVYEiFTnKVcGyE1koJhFFuFXlNMCE5TQwuU50JQIzjJ1zSXGe2D28PceP_XzoZWbdzON3GlIoJLygUSNLrIwWW8C8HbQm19fNZ3CiO1h6j-IKo9RHWEGEM3h1Blrf0PSMkplYz-AuFUbUc</recordid><startdate>20231101</startdate><enddate>20231101</enddate><creator>Zhuo, Yelin</creator><creator>Zhu, Jianquan</creator><creator>Chen, Jiajun</creator><creator>Wang, Zeshuang</creator><creator>Ye, Hanfang</creator><creator>Liu, Haixin</creator><creator>Liu, Mingbo</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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To solve this problem, a response surface method (RSM)-based approximate dynamic programming (ADP) algorithm is proposed in this paper. Since the value function can be directly obtained by RSM, the proposed algorithm does not need to iteratively approach them as existing ADP algorithms, which facilitates reducing the computing time. In addition, an improved generalized polynomial chaos (IgPC) method (i.e., an extension of RSM) is proposed to calculate the expectation of the value function of ADP in the stochastic environment. Compared with the Monte Carlo method, which is commonly used in existing ADP algorithms, IgPC requires fewer sampling scenarios while providing similar results. 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| subjects | Algorithms Chebyshev approximation Computing time Dynamic programming Energy management Heuristic algorithms improved approximate dynamic programming improved generalized polynomial chaos Monte Carlo simulation Polynomials Power system dynamics Power systems Randomness response surface method Response surface methodology Stochastic energy management System effectiveness Uncertainty |
| title | RSM-Based Approximate Dynamic Programming for Stochastic Energy Management of Power Systems |
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